Artificial intelligence has long since solved complex tasks and made our everyday lives easier. But do intelligent computer programs also provide new solutions for environmental and climate protection?
Unnoticed by many, artificial intelligence (AI) has long been intervening in our everyday lives—be it as a translation system, search engine, personal language assistant or robots programmed for specific activities. "The fact that AI will be an enormously important criterion for success in the future for science, society and especially the economy, is now sufficiently accepted," says Kate Saslow, project manager for AI and foreign policy at the German Stiftung Neue Verantwortung (New Responsibility Foundation). This is largely to do with the fact that the potential applications of intelligent computer programs are extremely versatile and, as a cross-cutting technology, connect diverse areas.
What has become clear is that AI is likely here to stay, and will only develop further in the future. Instead of being stuck debating the question of whether we need or want AI—and thus missing the chance to have a say in its development—we should be discussing how and where we can use AI to serve the general good of humanity.
Voice or face recognition, search engines, self-driving cars, computer games, social bots (chatbots that mimic human communication) or robots—all of these areas heavily involve AI.
But what exactly is artificial intelligence? So far, there is no single universally valid definition of AI. If we look for the lowest common denominator, artificial intelligence can be described as computer programs that can learn and improve themselves. They are intelligent to the extent that human cognitive decision-making structures are replicated by programming. This includes visual perception, speech recognition and generation, reasoning, decision-making and action, as well as the ability to adapt to changing environments.
Simple, "non-intelligent" algorithms (for example, navigation devices) are programmed to answer clear questions: this is the problem and this is how it is solved (e.g. "find the shortest way from A to B"). The very heterogeneous AI applications, on the other hand, are not based on classical deterministic programming, but on statistical data analysis. In a learning process—also called machine learning—the artificial system learns from examples based on training data, which it can generalise independently after the learning phase. An AI-supported translation system, for example, is fed hundreds of thousands of data in the training phase. It does not learn the examples by heart but focuses on patterns and regularities. In this case, it learns which word is translated into which word and which sentence into which sentence, which can then also be applied to new, previously unseen data.
The European market for artificial intelligence is predicted to grow 35 percent in 2024, according to Statista. The decisive factor for the rapid developments in the field of AI applications in recent years is the fast-growing computer power. This makes it possible to process the large amounts of data of rapidly advancing digitalisation. An end to these developments is not in sight. Computer scientists Dario Amodei and Danny Hernandez, who both work for OpenAI, a non-profit research company founded by, among others, Elon Musk, conclude in their study that an increase by a factor of 300,000 took place in the period from 2012 to 2017. This means that the new AI systems are currently, on average, doubling their speed every three and a half months and are thus able to process ever larger amounts of data.
AI is being treated as a key technology in many social, and especially economic, areas. Could it also transform our society towards ecological and social sustainability? In view of the urgent need for action to avert or at least contain climate collapse, a wide variety of projects, start-ups and research projects have set out to find new solutions with the help of intelligent computer programs.
In earth observation, for example, machine learning methods (such as pattern recognition and the linking of large amounts of data) together with data collected by satellites can help to make precise statements about climatic change.
In the energy sector, AI can help to regulate the growing complexity of the decentralisation of the energy system which goes hand in hand with the switch to renewable energy sources. It also helps us use infrastructures more efficiently and increase the flexibility of the energy system by intelligently networking energy systems (smart grids), intelligent building control and energy data management.
AI applications could also contribute to making the transport system more environmentally friendly, as well as assist in waste and recycling management.
For more examples of how AI is helping to protect wildlife and preserve our forests, click here: Sustainability and AI.
The topic of AI brings up several ethical dilemmas. Key questions include:
Autonomy: How autonomously does an AI act? The concept of autonomy of AI systems calls into question the self-determination of humans. This is a particularly key debate in the case of autonomous vehicles.
Trustworthiness and Transparency: How does an AI reach a decision? There is a certain lack of transparency in the principles of machine learning algorithms since the use of statistical correlations makes the algorithms more difficult to understand. For example, it is not always possible to deduce from an algorithm why a certain input value produces a certain output value. The reasoning that "the algorithm learned it that way from the sample data" is unsatisfactory for a deeper understanding. This can also become a problem from an environmental perspective; namely, when decisions made by AI systems cause environmental damage.
AI systems must be prevented from reproducing the discriminatory patterns that exist in society and from making unsustainable decisions. This can be done by ensuring algorithms are transparent and that the training data for algorithms is designed more inclusively.
Most start-ups, financial service providers and established e-commerce companies have long since integrated AI into their mobile applications, shopping systems or the data processing of their online stores. The intelligent algorithms classify and predict, suggest products to customers and make purchase recommendations.
What is worrying is that the dominance of the large IT and internet corporations (Google, Microsoft, Apple, Facebook, IBM, Amazon, etc.) is continuing in the AI sector, as in all other areas of the digital economy. These globally active providers of digital services now have a near-monopoly position and have huge pools of data at their disposal. From a sustainability perspective, the main problem is that digital corporations are using AI applications to further personalise their services. They can then create increasingly accurate forecasts about what customers want to buy, leading to more consumption. The German Federal Environment Agency study, “Consumption 4.0” states: "The lowering of technical barriers, the integration of purchase recommendations and evaluations into consumers' everyday lives via social networks and personalised marketing can encourage consumers to make more frequent purchases, limited only by their disposable income."
In addition to the discourse on the ethical risks of AI, the question of the life cycle assessment of AI systems is also relevant from a sustainability perspective. While some see enormous opportunities in the fact that we can tackle environmental challenges with AI, others fear it will merely create new problems. These include the further increase in our already enormous demand for electricity and massive increases in consumption.
The concerns are not unfounded: AI applications such as deep learning, simulations and forecasts require more and more computing power and will increase energy demand in data centres. As researchers at the University of Massachusetts have determined, training a single AI application for speech recognition can generate five times as much CO2 as a car emits over its entire lifetime.
AI models are often made up of Graphical Processing Units (GPUs) which are located on servers in data centres. Keeping data centres cool is a huge challenge that requires both energy and clean water, increasing the environmental impact of these servers and the AI models that use them. GPUs are also made from metals and minerals that require a lot of energy and processing to procure.
However, the possibility of AI models could lead to emission reductions in the long term. The emissions output of training AI should be weighed up against potential societal gains on a case-by-case basis.
Future AI applications could also have lower energy consumption, as there are still refinements that can be made, especially in the area of machine learning. "Many research groups are working on making deep learning less energy-hungry. Different hardware, new models and learning methods—all this is being investigated," says Prof. Dr Kristian Kersting, Head of the Machine Learning Department at the Darmstadt Technical University.
In December 2018, Germany published the German AI Strategy, which referenced sustainability concerns and opportunities of AI. The strategy outlined how evaluation criteria for the environmental impact of AI should be developed and an environmental data cloud should be established.
The plan to reach these goals included a specific funding program, "AI Lighthouses for the Environment, Climate, Nature and Resources", in which the Federal Ministry for the Environment (BMU) looks for projects from business, science and civil society that use artificial intelligence to tackle ecological challenges. 35 projects were approved during the first funding program and 46 million EUR was allocated. Projects included Nadiki, an AI system that aids resource management and reduces CO2 emissions, and DC2heat, a digital twin that optimises the use of waste heat from data centres. Since 2018, the AI strategy has been updated and more funding has been allocated to the promotion of AI, with 5 billion EUR now allocated by 2025.
In March 2024, the European Parliament published the Artificial Intelligence Act. These guidelines have been in discussion since 2021 and focus on outlining regulations for the development of artificial intelligence. The 500-page publication recognises the potential benefits of AI for climate protection while acknowledging the need to reduce the impact of AI on environmental sustainability.
The intersection of AI and climate change is being addressed worldwide by institutions, universities and researchers alike. In August 2024, the United Nations published an impact assessment on harnessing the power of AI for climate change. Increased amounts of funding are available for projects that focus on AI in the environmental field, from the BMUV in Germany to Horizon in Europe.
With this overview, we have set out to discover how far AI applications can contribute to environmental and climate protection. Two things have become clear. As initial projects show, AI applications certainly have the potential to support sustainable development by contributing to more efficient use of resources, providing us with accurate information about the state of our planet or making forecasts easier. But assuming AI leads to a more sustainable planet is not a foregone conclusion. There are ethical concerns, electricity consumption and the dominance of corporations (which could lead to growing consumerism) to consider.
For AI applications to contribute to environmental and climate protection, suitable framework conditions must be created. This includes, for example, guidelines that keep energy consumption within limits and a political framework that guarantees data protection and transparency. This also includes ensuring that data and AI systems are not controlled by just a few global players.
To drive innovative, sustainable AI developments, it's important to promote research and young start-ups while creating new interfaces between research communities. Currently, research communities that deal with AI and those that deal with climate and environmental problems work together far too little.
To the question of whether AI can save the world, the answer likely remains, no. AI technology alone will not save the world; other technologies won't save the world either. But artificial intelligence can make an important contribution—if we use its power for good.
This article was originally published in December 2019. It was updated in April 2022 by Marharyta Biriukova and in August 2024 by Kezia Rice.
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Digitalisation has long since arrived in agriculture. In the fields, GPS-controlled tractors take over sowing and crop care, in the barns, cows and pigs are fed by automatic machines and farms organise all processes via digital management systems.
Semi-autonomous agricultural machinery and farm management software have been used in agriculture for years. However, the pace of digitalisation has accelerated. A rapidly growing number of sensors, cloud-based IT systems and, last but not least, artificial intelligence are finding their way onto fields and farms. In Germany, France and the U.S., only around 13 percent of farmers don't use digital technologies, and only 5 percent in Brazil. In Japan, however, around 40 percent of farmers who responded to Continental's 2024 study that they carry out their agricultural work without digital applications.
Most digital applications in use today are aimed at reducing production costs by increasing efficiency. However, this can also have another effect—greater efficiency ideally leads to a lower use of resources. This can not only reduce costs for farmers, but also CO2 emissions and harmful environmental impacts. For example, if the routes of agricultural machinery are optimised using GPS, unnecessary routes are eliminated and less fuel is required. And if sensors, drone images and satellite images are used to determine how much water, pesticides or fertilisers a plant actually needs, these can be used in a more targeted manner and hopefully more sparingly.
In politics and science, digitalisation is therefore seen as playing a key role in the drive towards sustainable agriculture. However, despite the increased use of digital technologies, there has been no visible reduction in the environmentally harmful effects of agriculture to date. The continued massive use of pesticides and lack of protected zones is weakening many species, while the monocultures and over-fertilisation that dominate our fields are leaching the soil. This in turn can have a negative impact on agricultural yields. What's more, is that only healthy soils and ecosystems can store large quantities of CO2 emissions.
Agriculture also contributes to the planet's CO2 emissions. Estimates indicate that net global greenhouse gas emissions from agriculture including forestry and other land use contributed to around 21 percent of total global greenhouse gas emissions in 2024. Livestock farming is the main cause of the high emissions.
Agriculture itself is also suffering from the negative effects of climate change. As early as 2022, 67 percent of the farmers surveyed in a study by Bitkom study stated that their businesses would be affected by the consequences of climate change. Droughts, floods and other increasingly frequent weather extremes have a negative impact on yields and make farmers' work more difficult.
It is therefore essential to dismantle and restructure the livestock industry and reduce the consumption of animal products in order to reduce emissions from agriculture. To ensure healthy soils and preserve biodiversity, the use of fertilisers and pesticides must be restricted and important biodiversity hotspots must be removed from agricultural use. Organic farming should also be strengthened and diverse, small-scale agriculture promoted.
There is broad scientific consensus on these measures. And if implemented consistently, they could not only reduce the negative effects of agriculture, but even have a positive impact on the environment and climate. This is also emphasised by agronomist Prof. Dr Sonoko Dorothea Bellingrath-Kimura from the Leibniz Centre for Agricultural Landscape Research (ZALF) in an interview with RESET. "Overall, agriculture can increase soil fertility and also improve soil structure." It can also make an essential contribution to biodiversity and groundwater formation.
On the one hand, it can be said that agriculture is becoming increasingly digitalised and that this harbours potential for environmental and climate protection. On the other, these developments do not yet appear to be bringing about the decisive agricultural turnaround everyone wanted. Why is this? What potential is not being utilised, and what are the key prerequisites for a sustainable digital agriculture revolution?
To answer these questions, we have identified promising sustainable digital solutions as part of our in depth feature, Sustainable Agriculture.
When does a plant need water or nutrients—and exactly how much? As a rule, plant nutrition is based on estimates. Precision agriculture replaces this experience with measured values from sensors, drones and satellite imagery.
The startup constellr, a Fraunhofer EMI spin-off, has been working since 2023, for example, on setting up a constellation of 16 shoebox-sized satellites that record fields from space. With the help of a new, high-precision thermal infrared measuring instrument, the technology is able to determine the health status and water requirements of crops.
The application of water, fertiliser or pesticides based on this information is increasingly automated using GPS-supported guidance systems for agricultural machinery or irrigation systems. This allows plants to be controlled in an increasingly targeted manner.
According to calculations by constellr, the technology could save 180 million tonnes of water and 94 million tonnes of carbon dioxide per year globally in the future—with an increase in crop yields of up to four percent.
Ecological potential of precision agriculture
Savings on fertilisers: Various studies have most frequently identified savings in fertilisers (Saiz-Rubio & Rovira-Más, 2020; Villa-Henriksen et al., 2020). According to a meta-study, the site-specific application of fertilisers can reduce nitrogen residues in the soil by 30 to 50 percent.
Fewer pesticides: Another major ecological potential of precision farming is the reduced use of pesticides. With conventional spraying methods, only around ten per cent of the pesticides applied actually reach diseased plants. Greater precision in application, including "spot farming" of individual plants, can have positive effects on biodiversity and reduce environmental damage.
Water consumption: The actual reduction in water consumption is more ambivalent. It is true that most of the applications analysed can reduce direct water consumption in irrigation. The aforementioned study was able to demonstrate average savings of eight percent in plant cultivation and a robot gardener was able to reduce water consumption by half. However, the variance is large.
Harvesting robots have been traversing our fields for several years now. However, currently, they fertilise and harvest food with a bulldozer rather than a velvet glove. Agricultural robots have therefore so far been exclusively suitable for robust foods such as potatoes, cereals or beetroot.
Modern robotics will change this in the next few years. The latest generation of robots uses sensors, cameras and AI software to carefully separate the ripe fruit from the plant. The picking robot from Floating Robotics can pick tomatoes without damaging them. At the same time, it has enough power to unfold fruit crates and transport them away for processing.
Another focus of robotics in agriculture is plant care. Small, autonomous robots can remove weeds and determine the health of plants. With the help of their cameras and AI, they recognise diseases and can treat the affected plants promptly and specifically.
The main task of robots in fields and stables is to reduce the amount of labour required on farms. But, they also have the potential to contribute to environmental protection.
Ecological potential of robotics
Protecting soils and biodiversity: Pesticide use is set to become much more precise thanks to robots. And if the robots destroy weeds in a targeted manner using electric shocks, agrochemicals could even become completely unnecessary. With a sufficient data basis and with the help of machine learning, robots could also specifically recognise protected plants or weed plants that serve as insect food and protect them during mechanical weed control.
Less soil compaction: Another opportunity for field robots in agriculture is that they can replace heavy agricultural machinery. Large machines compact the soil through their weight alone and promote erosion. This has a negative impact on water and nutrient cycles, the availability of oxygen and therefore also on the occurrence of microorganisms.
Smaller-scale cultivation systems with greater diversity: Field robots, but also other light and flexible machines such as drones, offer the opportunity to cultivate smaller areas with greater diversity.
However, the use of robotics is still largely in the development phase.
A project at Benha University in Egypt demonstrates the possibilities that smart monitoring can offer for the future of agriculture. The researchers used GIS data and machine learning to calculate the soil quality of a huge area in the Nile Delta. This enabled them to determine the soil quality of the Nile Delta based on a small number of samples and use the data to make specific recommendations for areas with poor soil quality.
Ecological potential of smart monitoring
Simplified soil quality measurements: Measurement methods that collect and process data using satellite data, machine learning and other digital technologies significantly simplify soil quality monitoring. In the future, fixed sensors could also provide data for regular calculations of the environmental needs of entire regions.
Improvement of ecosystem services: Smart monitoring can also be used to improve the understanding of interrelationships and ecosystem services on agricultural land. High-resolution sensors can support the classification of habitats, for example, which can be used as a basis for modelling biodiversity. This makes it possible to visualise places where birds and insects cavort and implement more targeted measures to protect them.
However, data collection and analysis with regard to biodiversity indicators is still in its infancy.
Using apps and desktop applications, digital management tools help to optimise agricultural processes and manage farms more easily. For example, yields, fertiliser and pesticide application rates, machine data and site-specific data as well as weather and soil data are linked via the applications, increasingly with the help of machine learning.
However, the application of such systems is often cost-intensive, complex and not all devices and software types on a farm are compatible. There is also a risk of lock-in effects, as the platforms are predominantly provided by start-ups, multinational tech companies and food and agricultural groups. This can mean that users are tied to a specific provider ecosystem via the digital services. Once here, they are encouraged to buy other agricultural products from the provider, such as seeds, inputs and machinery, as Kevin Cussen from LiteFarm reports.
The free open-source platform LiteFarm focuses on cooperation in development and aims to achieve sustainable agriculture. All functions are specially tailored to the needs of organic farmers. Compared to many other platforms, LiteFarm offers important information for diversified farms and has a large catalogue of plants that users can expand independently and share with the LiteFarm community. The platform also offers support with the organic certification of farms.
Ecological potential of FMIS
Saving resources: In addition to making work easier for farmers, the consolidation of data has the potential to plan processes in line with requirements and, in the best case, to make them more efficient. This can save on fertilisers and pesticides, among other things. If the FMIS are also tailored to sustainable cultivation practices, they facilitate their use and dissemination.
Support for small farms: The platforms provide valuable support, especially for small and diversified farms, as they facilitate their management. Simplified management of agri-environmental measures: Many environmental measures are currently not accepted by female farmers because they are complicated to apply for or labour-intensive to implement. FMIS offers the opportunity to simplify applications, documentation and monitoring in the future, as all information can be collated here and forwarded automatically.
What the technologies mentioned in the examples have in common is that sustainable aspects such as the protection of soil and biodiversity were among the objectives of their development from the outset. This sets them apart from most other digital applications, which are primarily concerned with making agriculture more efficient and thus saving costs. Sustainability is, at best, only a side effect here.
However, while politicians and companies are focusing on digitalised agriculture's potential, the risks are still not being given enough attention. But what exactly are they?
A major risk of digitalising agriculture is that technologies, such as precision farming and automation, have so far been used by farmers primarily to level up agricultural yields. This means that improved efficiency does not ultimately lead to savings in resources such as fertilisers and agrochemicals, but rather, these are invested directly in further increases in yield or the expansion of cultivated areas. Tilman Santarius, Professor of Socio-Ecological Transformation, has coined the term "rebound effect" to describe this. Put simply, better farming can mean more farming, with all of the chemicals and land requirements implied.
The same applies to irrigation. There is evidence that farmers are irrigating more fields and growing more water-intensive crops as a result of the more targeted distribution of water.
And if we look at field robots and drones, their advantages for cultivating small fields—their flexibility, autonomy and precision—also harbour the risk of opening up previously fallow or hard-to-reach sites. However, these ecological niches are home to many species whose habitats are being destroyed as a result.
Homogenisation of the cultivation systems:
Many digital systems are designed to optimise the cultivation of a very small number of high-yielding varieties. The high concentration in the market for digital cultivation systems further increases the risk. And the problem of lock-in effects has already been mentioned in connection with the LiteFarm project. The problem is that a low diversity of varieties can have a negative impact on the ecological resilience of agriculture, among other things.
Material and energy consumption:
If the ecological risks of digitalisation are to be considered comprehensively, the material and energy consumption of digital devices, cloud solutions and other infrastructures must also be included. However, no figures are yet available specifically for agriculture. However, material and energy consumption is likely to be rather low compared to the consumer electronics sector, for example—for now.
So whether digital technologies can actually realise a potential for biodiversity and nature conservation and contribute to a reduction in CO2 emissions depends on their objective and the framework in which they are developed.
Set incentives:
Whether there is an incentive for developers to develop sustainable technologies and for farmers to utilise them depends on the political framework conditions, such as environmental regulations and subsidies. Currently, the focus here is very much on production, yield and land use. "You get premiums for agricultural use," says Bellingrath-Kimura. "The more you produce, the better. This means that there is no incentive to become more sustainable, i.e. to use resources more efficiently or to protect biodiversity."
In contrast, it would be important to view sustainable goals—such as the preservation of biodiversity or the storage of CO2—as management goals as well and to give them an economic value. This must be reflected in agricultural funding structures at both federal and EU level. The Green Deal and the EU's "Farm to Fork" strategy, which aim to promote the transformation of sustainable agricultural and food systems, are at least the first important steps.
Ensure data availability and data sovereignty:
Access to and ownership of data is central to the question of whether and how digital technologies are used and who benefits from them. On the one hand, many farmers are critical of the use of their data in digital applications and services. At the same time, a comprehensive database improves the quality of the technologies and is an important basis for new developments. Clear legal requirements should therefore ensure transparency, security and fairness between farmers and technology manufacturers. This includes farmers retaining ownership of the data and being able to decide on its use themselves.
In addition, it is essential that geodata, weather data, satellite data and other data that is important for agriculture is made available and publicly accessible to farmers, research institutions and public advisory centres.
Create open interfaces:
As the various examples show, there is already a wide range of technologies that make work in the fields easier and support environmental protection. For comprehensive monitoring and effective protective measures based on this, it is essential that the various digital solutions can be networked with each other across manufacturers. Open interfaces should therefore be considered right from the development stage.
Enable co-operation:
The discourse between agriculture and environmental and climate protection is often very polarised and both sides feel misunderstood. One of the reasons for this is that generalised scientific recommendations for more environmental protection do not always fit the specific situation on the ground. In addition, many farmers feel that their work is being hindered by ever more bureaucracy and regulations. The focus should therefore be on cooperation so that priorities for the digital transformation of agriculture can be jointly explored. In this way, the farmers' experience can also be incorporated.
Advancing research:
As previously mentioned, there are many studies that deal with the opportunities of digitalisation in agriculture, but little research on the actual effects of digital applications on biodiversity, soils and climate. There is therefore an urgent need for comprehensive and independent studies both on individual digital technologies and on the overall potential of digitalisation.
Orient funding programmes towards sustainability:
Improving efficiency alone is not enough for digital technologies to really help overcome environmental challenges. Priority should therefore be given to promoting innovations that are consistently geared towards sustainability. They should also be applicable and profitable for smaller companies. Public funds in particular must be targeted and prioritised for investment in research into transformative technologies.
So what could the agriculture of the future look like in which sustainability and digitalisation go hand in hand? The vision of the future that agronomist Sonoko Bellingrath-Kimura outlined in an interview with RESET is an agriculture in which measures to protect biodiversity and species are carried out automatically, made possible by smart networking. Comprehensive monitoring using sensors and satellites makes biotopes with high biodiversity and other places worth protecting visible. "If you know that a place is very important as a biotope, you could exclude this small part from productive measures and not fertilise or spray it, for example. But then it would be best to switch off the sprayer automatically at this point without having to get out of the tractor and turn round."
This can work if the steering systems of the tractors or robots are linked to the management and information software. And it is not only the routes of the machines that are then planned and controlled. The applications for biodiversity measures are also linked to it and transmitted automatically. Bellingrath-Kimura is convinced that this consistency will make it easier to implement many measures to protect soils and biodiversity.
As the examples should have shown, the technologies for this are already in the starting blocks. What needs to happen now is to align all digital applications to sustainability with an appropriate framework.
Finally, however, it is important to narrow things down. Of course, digitalisation alone will not bring about the total agricultural revolution that many have been asking for. That would require just the right balance of nature-based and digital solutions.
Floods in Spain. Fires in Patagonia. A heatwave in Rio de Janeiro. Open the news and you’ll be hit with stories about extreme weather events across the globe. As our climate becomes increasingly deadly, predicting the weather has become a lifesaving task.
Weather forecasts are also indispensable for society; from food production to safe transport to generating energy with wind or solar power. But, the supercomputers that create weather forecasts have a huge carbon footprint. The UK’s Met Office admitted to using a supercomputer that emits 14,400 tonnes of carbon a year. Paradoxically, these forecasting tools contribute to global warming, leading to more of the extreme weather events that they’re designed to predict.
In March 2025, researchers from the Alan Turing Institute, the University of Cambridge, Microsoft Research and the ECMWF published a blueprint for a new way of predicting weather. Known as ‘Aardvark’, the AI-model would allow weather to be forecast on a desktop computer. This would use thousands of times less computing power than the current supercomputers that create weather forecasts.
Numerical Weather Prediction (NWP)—creating weather forecasts with supercomputers—has come a long way since its inception in the 1950s. Back then, it took 24 hours to create a single-day forecast. Accuracy and speed have greatly improved, but it still takes hours for NWP supercomputers to process data in order to tell us if tomorrow will bring rain or shine. And of course, all those hours running supercomputers requires massive amounts of energy from data centres.
Aardvark is much faster than NWP, taking just minutes to run on a desktop computer. The AI-model has been trained on data from weather stations around the world. With just 10 percent of input data, Aardvark is already able to generate some forecasts that are more accurate than the United States GFS forecasting system. And, it will be able to produce accurate eight day forecasts, compared to NWP which can only accurately predict up to five days of weather. The research team also outlines the potential for Aardvark models to be adapted to specific regions. This fine-tuning based on regional differences would improve accuracy when predicting weather.
Users of NWP systems can easily switch to using Aardvark. And, areas of the Global South who don’t use NWP due to a lack of resources can also adopt the model. The research team wrote in their paper that, “The results presented in this study only scratch the surface of the potential of Aardvark Weather.” They are currently implementing the technology into existing studies at the Alan Turing Institute, while exploring how to deploy Aardvark in the Global South. While it’s not yet stated when Aardvark could replace NWP as our go-to method for predicting weather, the forecast for its future looks bright.
" ["post_title"]=> string(87) "AI-Model Aardvark Forecasts Weather Without the Need for Carbon-Emitting Supercomputers" ["post_excerpt"]=> string(139) "AI-model Aardvark can predict weather faster and more accurately than existing systems—all while emitting thousands of times less carbon." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(87) "ai-model-aardvark-forecasts-weather-without-the-need-for-carbon-emitting-supercomputers" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2025-05-21 12:12:26" ["post_modified_gmt"]=> string(19) "2025-05-21 10:12:26" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=116207" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [3]=> object(WP_Post)#6689 (24) { ["ID"]=> int(115040) ["post_author"]=> string(5) "21514" ["post_date"]=> string(19) "2025-01-27 06:57:00" ["post_date_gmt"]=> string(19) "2025-01-27 04:57:00" ["post_content"]=> string(6893) "From the 27th to 30th of December 2024, the hacker community met for the annual congress of the Chaos Computer Club (CCC) in Hamburg. Known as 38C3, it's one of the largest hacker conferences in the world with more than 12,000 participants. The focus is traditionally on technical and political issues relating to security, cryptography, privacy and freedom of expression on the internet. And of course there is soldering, coding and hacking. Every year, newly discovered security vulnerabilities make the headlines. At 38C3, the focus was on the new electronic patient file (ePA). In several presentations, hackers demonstrated how easily they could gain access to sensitive data using simple tricks. And even our street lighting and the control of wind and solar parks are not safe from hackers.
I spent two days travelling around the congress for RESET. What particularly interested me was the presence of topics relating to sustainability in IT. I actually heard a lot of talks on the topic, including on the big stages. The Bits & Bäume (Bits & Trees) network also had its own habitat with exciting talks, workshops and networking opportunities. Definitely a good place to go for all those who are concerned with the ecological footprint of digitalisation.
The special atmosphere of the hacker congress can only truly be experienced in the hustle and bustle of the conference halls. But several presentations are available to watch online. Here are my recommendations – it's worth taking a look!
Resource Consumption of AI – Degrow or Die
You probably know that the energy consumption of AI is exploding. What less people know, however, is the high consumption of resources such as water and metal. In this presentation, Thomas Fricke gives an overview of the devastating effects of data centres on our environment and presents possible de-growth scenarios. How much energy can be saved through alternative technologies?
What comes after Windows 10? Time to switch to open source software!
Extending the lifespan of hardware via free software is positive for both users and the environment. The presentation by Joseph (KDE Eco) is an invitation to venture into the open source world.
7 Years Later: Why And How To Make Portable Open Hardware Computers
The presentation by Lukas ‘minute’ Hartmann (MNT Research) shows problems and solutions in the development of open hardware laptops with a minimal budget (sound quality improves at 4 minutes).
What is genome editing, CRISPR/cas, RNAi or off-target effects? How do generative AI and generative biology come into play here? Magret Engelhard (Federal Agency for Nature Conservation) gives an overview of what is happening in laboratories around the world. And she shows how big tech is entering the bioeconomy. Titans like Google, Alibaba or Amazon are now leading this new race – without specific knowledge of life sciences.
OpenPV - Calculate the solar potential of your building
With OpenPV, the photovoltaic potential of roofs and facades can be simulated in real time using open data. In this presentation, the developers present their open source website. It deals with open geodata, physics-based simulations of solar radiation, some shady WebGL code and insights into the financing possibilities of open source projects.
From Silicon to Sovereignty: How Advanced Chips are Redefining Global Dominance
Extreme Ultraviolet (EUV) lithography is a key method in creating state-of-the-art chips for our devices. But sourcing the raw materials poses challenges. The talk covers the importance of this technology and how different countries control the supply chain of EUV.
" ["post_title"]=> string(67) "Hacker Congress 38C3: All About Sustainability in the Digital World" ["post_excerpt"]=> string(115) "The Chaos Computer Club's annual conference took place at the end of 2024. Here are the top presentations to watch." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(66) "hacker-congress-38c3-all-about-sustainability-in-the-digital-world" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2025-01-08 15:36:43" ["post_modified_gmt"]=> string(19) "2025-01-08 13:36:43" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=115040" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [4]=> object(WP_Post)#6690 (24) { ["ID"]=> int(114810) ["post_author"]=> string(5) "21514" ["post_date"]=> string(19) "2025-01-06 07:08:00" ["post_date_gmt"]=> string(19) "2025-01-06 05:08:00" ["post_content"]=> string(6655) "For refugees or asylum seekers who arrive in a new country, the initial location they settle in plays a crucial role in their ability to integrate. Employment opportunities, available school places and community infrastructure are all necessary elements of a successful resettlement location. For refugees that relocate via resettlement schemes (114,300 in 2022, according to the UN), their new location is decided by caseworkers who are trained in taking the nuanced circumstances of an individual into account to consider where they might have the best chance of success.
Michael Hotard describes the first location refugees settle in as “a stumbling block or a stepping stone as [they] move towards a new life”. Hotard is the Director of GeoMatch, a software tool developed by researchers at the Immigration Policy Lab (IPL) at Stanford University and ETH Zurich. GeoMatch is provided to governments and non-profits to assist them in finding the most suitable location for refugees to resettle. The tool saves these organisations countless hours of manual data checks, instead using a machine learning model to provide location recommendations based on previous experiences of refugees in that location. Tests of GeoMatch’s algorithm using historical data resulted in refugees being twice as likely to find a job, compared to refugees who had been resettled without the aid of the model.
Hotard explained to RESET how he and his team train GeoMatch’s algorithm. “The first step is building predictive machine learning models using historical data from refugees and asylum seekers who have arrived in the country.” This data includes both demographic information and details of an individual’s economic situation: namely, whether or not someone in the family obtained employment. Then, a resettlement organisation applies the models to a newly arriving refugee or family who they are looking to resettle. “[GeoMatch] makes a prediction against all available locations about where that individual or family is most likely to succeed based on previous outcomes,” Hotard said. The algorithm then checks the capacity of each location, before providing a list of recommendations to the case decision maker.
So, does this mean that the fate of refugees is in the hands of a machine? Not quite. “We’ve designed GeoMatch as a recommendation tool,” Hotard explained. “There’s always a human decision maker considering that recommendation and making that final choice.”
“We take data privacy concerns very seriously,” Hotard tells us. “All data is de-identified so the tool is not receiving names, contact information or addresses. It only receives the information that’s needed.”
The ethics of AI is another concern. Artificial intelligence has been known to have racist bias, while data sets that train AI often exclude women thanks to the gender data gap (a bias in data sets that results from a lack of sex-disaggregated data). How does GeoMatch ensure its algorithm doesn’t fall foul of bias? “We always test GeoMatch’s predicted impact on various subgroups,” Hotard says. This involves ensuring GeoMatch’s location recommendations consider the benefits for different genders and nationalities. They also consider what languages an individual speaks, as well as their level of education.
GeoMatch has received positive feedback from the non-profits who have begun using the tool. Ultimately, the time saved by GeoMatch enables caseworkers to focus on the refugees and asylum seekers they work with. But, GeoMatch doesn’t replace the work of these caseworkers by blindly sending refugees to new locations. As Hotard describes it, GeoMatch “is a tool that empowers people to make more informed decisions.”
" ["post_title"]=> string(111) "“The First Location Is a Stumbling Block or a Stepping Stone”: How AI Model GeoMatch Is Relocating Refugees" ["post_excerpt"]=> string(133) "Refugees who settle in a new country face location-based challenges. Could artificial intelligence model GeoMatch provide a solution?" ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(92) "the-first-location-is-a-stumbling-block-or-a-stepping-stone-the-ai-model-relocating-refugees" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2025-01-08 12:38:43" ["post_modified_gmt"]=> string(19) "2025-01-08 10:38:43" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=114810" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [5]=> object(WP_Post)#6719 (24) { ["ID"]=> int(112992) ["post_author"]=> string(5) "21506" ["post_date"]=> string(19) "2024-06-12 07:30:00" ["post_date_gmt"]=> string(19) "2024-06-12 05:30:00" ["post_content"]=> string(6691) "Do you actually know what's growing in your garden? Or on the roadside that you pass every day? No idea? Don't fear—the Flora Incognita app identifies more than 16,000 plant species for the curious horticulturalist. Simply take a picture of the mystery plant in question and receive its name and a description in a matter of seconds. You'll also get information on the characteristics, distribution and conservation status. But the app can do even more: every plant captured with the camera also provides important data for biodiversity research.
Behind the app is an AI that analyses all images with a sequence of deep neural networks on the Flora Incognita computer cluster. However, this also comes with a drawback: the images and metadata can only be transferred to the server with an internet connection. Therefore, no results can be expected in areas without strong enough network coverage, which could rule out more remote areas. However, images of plants taken with the app can also be identified later, like with other identification apps such as Spotify. All important metadata associated with this image is retained in offline mode.
According to the company, the Flora Incognita app has been downloaded over 5 million times since 2018—and over 300,000 identification requests are received daily. As a result, the digital reference work has not only established itself among plant experts but has also become an important authority in biodiversity monitoring. Thanks to its widespread use, the plants are now documented in real-time on an unprecedented scale. And this is more important than ever.
Alongside the climate crisis, biodiversity loss is one of the greatest threats to humanity. When species become extinct or new species immigrate, this can destroy entire ecosystems. And since all ecosystems are ultimately interconnected, this can have far-reaching consequences. Comprehensive monitoring of plant biodiversity is therefore essential for species conservation, as it enables changes to be recognised earlier.
However, monitoring is challenging because the need for effective conservation measures also increases the need for spatially and temporally high-resolution surveys. At the same time, the population's knowledge of species is declining.
A wide variety of methods are already being utilised for monitoring. Satellite images can be used to recognise the greening of entire areas of land and cameras in treetops create automated image series from above. However, it is difficult to recognise specific details about which plants are around and in which growth phase or state they are currently in. The Flora Incognita app and their hard-working plant collectors are closing this gap.
We humans have one thing in common with insects: attraction to colourful flowering plants. That's why most photos taken in the app are of plants in full bloom. The thousands of requests over the several years of the app's existence have resulted in a wide pool of observation data. This shows, for example, whether the flowering periods are shifting. This helps scientists to better understand the effects of climate change on biological systems. The observation data from the Flora-Incognita app complements the data collected by, for example, the German Weather Service.
With the help of the observations, supra-regional phenological patterns could also be detected. These include, for example, the later flowering of many species in northern and eastern Europe and a Europe-wide shift in the start of flowering between years.
Another possible application of the Flora-Incognita data is the monitoring of invasive plant species. These often pose a threat to native biodiversity. However, controlling and combating them is associated with high costs for our society. Early detection and a rapid response are crucial to prevent the spread and establishment of such invasive species. Flora Incognita can make a valuable contribution, as invasive species also end up in front of users' cameras. The documentation of plant occurrences at a specific time in a specific place thus creates a growing and robust source of data - across national borders.
The Flora Incognita app was developed by the Ilmenau University of Technology and the Max Planck Institute for Biogeochemistry and is currently available free of charge in 20 languages for Android, iOS and Harmony OS devices.
Flora Incognita is constantly being developed further. Gradually, high-quality profiles for all supported languages are being added and a global reach is also being sought. In addition, profiles specifically for children are to be integrated. If several profiles are available, the desired profile can be selected via the app settings.
" ["post_title"]=> string(88) "Identify Plants With the Flora Incognita App - And Support Important Biological Research" ["post_excerpt"]=> string(151) "AI-supported plant identification app Flora Incognita provides users with the names of previously unknown plants, turning you into a citizen scientist." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(65) "identify-plants-with-the-flora-incognita-app-and-support-research" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2024-06-11 15:48:03" ["post_modified_gmt"]=> string(19) "2024-06-11 13:48:03" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=112992" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [6]=> object(WP_Post)#7064 (24) { ["ID"]=> int(112714) ["post_author"]=> string(5) "21514" ["post_date"]=> string(19) "2024-05-22 07:00:00" ["post_date_gmt"]=> string(19) "2024-05-22 05:00:00" ["post_content"]=> string(8679) "Utqiagvik (formerly known as Barrow) is a small town in Alaska, at the northernmost tip of the United States. Its 4000-strong population rely on fishing in the Arctic Ocean as part of their food source. But in recent years, fish stocks aren’t what they used to be. Locals have begun finding wild Alaskan salmon along with their usual catches. Delicious, yes. But why are they appearing at all?
Every year, winter sea ice melts and leaves a cold pool (a stretch of very cold water) near the seafloor in the Arctic Ocean. The cold pool is a boundary that keeps Arctic species separate from sub-Arctic species. But climate change means the sea is warmer, ice is receding and the cold pool isn’t forming as it should. Predators are swimming further north and disrupting the usual food chains. And salmon are appearing in Utqiagvik, a sure sign that something is amiss with the Arctic Ocean.
“The future isn’t behaving like the past,” Leslie Canavera explains to RESET from her home in Virginia. Canavera is CEO and Co-Founder of PolArctic, a Startup that’s using AI modelling techniques to create a digital twin of the Arctic. She describes how indigenous communities used to be able to predict how ice in the Arctic would behave and knew the knock-on effects upon their fish stocks. While in 1990, 82 percent of fish stocks in the Arctic were at biologically sustainable levels, this number dropped to 65 percent by 2019, according to the FAO. “A lot of practices that people have relied upon and understood to be true [are no longer applicable],” Canavera continues. “Not having that baseline is really challenging going forward.”
PolArctic is co-founded by Canavera and Lauren Decker, both Alaska Natives. They use artificial intelligence and machine learning to map out and predict changes in the Arctic Ocean, providing fishing and shipping industries alike with the information they need to adapt to climate change in the region. Previous models of the Arctic use statistical modelling techniques. But this relies upon the assumption that the Arctic will continue to behave as it always has, not accounting for the elephant in the room: climate change is melting ice in the Arctic at alarming rates. PolArctic’s AI-based modelling techniques use historical data too, but are also able to learn systems and trends, forecasting climate impacts on the Arctic much more accurately.
The data that PolArctic uses to train its models doesn’t only come from Western science, but also from the indigenous knowledge of local communities in the region. Canavera wrote for the WWF that “AI and indigenous culture are often positioned as if in conflict with each other,” but explains how the success of PolArctic’s projects would not be possible “without the benefits of both.” With an estimated 370 million indigenous people whose livelihoods are negatively affected by climate change, collaborative solutions are urgently needed to help these communities survive.
PolArctic is nearly 18 months into its latest project: mapping out a digital twin of the Arctic. They aim to release it to industries in January 2025. But how exactly do you begin replicating the Arctic Ocean’s 14 million square kilometres, with over 240 fish species and diverse marine life? Canavera gives us an insight. PolArctic uses an agent-based modelling technique, programming each fish as an agent with all their varied characteristics. Simultaneously, the region’s landscape is mapped out in great detail.
Then, it gets interesting: the model is able to give advice, such as “fish school A, not school B, because school B needs to grow to ensure the biodiversity of fish stocks,” Canavera says. “You can then start asking the model questions,” she continues. “What if the ice doesn’t reach the same region next year?” The information the digital twin provides can help fishing industries adapt and boost profits, all while increasing the amount of fish in the Arctic despite the effects of climate change.
Although they are thousands of kilometres away from one another, the Arctic Ocean and Europe are intrinsically linked. Pressure systems that keep cool air in the Arctic and warm air in the south are being disrupted by climate change, leading to the heat waves, cold spells and extreme weather that are becoming increasingly commonplace in Europe. Meanwhile, microplastics from as far away as France have been found in the Arctic Ocean, highlighting the long-term effects that our growing amount of waste has on the planet as a whole.
Despite this, PolArctic’s digital twin offers hope that the Arctic may be able to adapt to the effects of climate change. Canavera relays the excitement of both indigenous communities and the fishing industry, who are eagerly anticipating the launch of the digital twin. In the words of Canavera, “our model has the potential to be game changing.”
" ["post_title"]=> string(76) "Why a Digital Twin of the Arctic Has “The Potential to be Game-Changing”" ["post_excerpt"]=> string(192) "PolArctic, a start-up using modelling technology and artificial intelligence to create a digital twin of the Arctic, will map out existing climate impacts on the area – and predict new ones." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(112) "the-potential-to-be-game-changing-how-a-digital-twin-of-the-arctic-is-helping-the-region-adapt-to-climate-change" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2024-09-06 15:19:09" ["post_modified_gmt"]=> string(19) "2024-09-06 13:19:09" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=112714" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [7]=> object(WP_Post)#7059 (24) { ["ID"]=> int(112396) ["post_author"]=> string(5) "21506" ["post_date"]=> string(19) "2024-05-08 06:00:00" ["post_date_gmt"]=> string(19) "2024-05-08 04:00:00" ["post_content"]=> string(15058) "Monocultures, compaction and rising temperatures — our soils will offer significantly poorer conditions for agriculture in the coming years. It is therefore essential that farmers protect their soils better to maintain their fertility. Smart monitoring could provide farmers with the information they need.
A project at Benha University in Egypt demonstrates the possibilities that smart soil monitoring can offer for the future of agriculture. The researchers focussed on GIS data, using machine learning to calculate the soil quality of a huge area in the Nile Delta.
Measuring the soil quality of a 2,300 square kilometre area using conventional methods is a mammoth project. Using conventional methods, researchers would have to scour the entire area, collecting enough samples to obtain just a rough map. In contrast, the researchers at Benha University were able to calculate soil quality in the Nile Delta using data from just a few samples.
32 soil samples up to 30 centimetres in size were taken from various locations along the Nile Delta. Their chemical and physical properties were then analysed in the lab, before being combined with preexisting data from a geographic information system (GIS). The researchers were then able to draw conclusions about the soil quality of the entire area from the samples.
The data, which was interpolated using mathematical functions and algorithms, provided specific recommendations for action in areas with poor soil quality.
For example, in order to improve soil quality in the area under investigation, the recommendation was made to plant more salt-resistant plants and prevent soil leaching with the help of water drains. The method could also be used to test whether the measures actually impact soil quality in critical areas.
If such measurement methods become established, we will be able to monitor soil quality much more easily in the future. Permanently placed sensors at a few locations could then provide regular calculations of the environmental needs of an entire region. However, researchers see even greater opportunities for consistent monitoring.
The Federal Agency for Nature Conservation (BfN) proposes an "Environmental IoT" for effective monitoring. In other words, a network of sensors, smart agricultural robots and other devices connected via the internet. Additionally, the "prediction of soil properties using a combination of nanosensors and machine learning could support the preservation of soil quality", similar to the approach taken by the Egyptian researchers.
Smart monitoring can also be used to improve the understanding of interrelationships and ecosystem services on agricultural land. High-resolution sensors can support the classification of habitats, for example, based on which biodiversity can be modelled. And, if land management and the application of agrochemicals are then adapted to the conservation of ecosystems, consistent monitoring can serve to protect species. If the places where birds and insects can be found are known, targeted measures can be implemented to protect them. This in turn leads to the preservation of plant biodiversity and better soil quality.
However, data collection and analysis with regard to biodiversity indicators is still in its infancy. Digital technologies are increasingly being used in agriculture and all the sensors, drones and management systems are collecting a wide range of data. However, these are primarily used to optimise processes with the aim of reducing costs for farmers and increasing yields. Data that allows conclusions to be drawn about biodiversity and the wider context of ecosystems is more of a by-product. This makes it all the more important to promote applications in this area and make the data available for targeted environmental protection measures.
Their risks should not be ignored.
Agricultural data, with its combination of location data and information from environmental analyses, is extremely sensitive. In order to be used effectively, however, it should be available to as many farms and companies as possible. However, the data often remains with the providers of agricultural software and it remains unclear whether and how it is processed and passed on.
This is why the anonymisation of personal data and open interfaces should be taken into account when developing digital solutions, as Dr Sonoko Bellingrath-Kimura emphasises in an interview with RESET. This is an "essential point in order to achieve the necessary networking" and then "everyone can use and develop it, regardless of whether they are large corporations or start-ups".
The data should also belong to the farms so that they can decide where the data flows go. Currently, clauses on automatic data flows in contracts for the purchase of sensors and machines often lead to farmers being sceptical about new technologies.
Such framework conditions will be particularly important if the agricultural revolution is to benefit from the current AI boom. The start-up Hortiya is already demonstrating the possibilities of "plant AI" at the greenhouse level. Using sensors and a "language model of the plants", the application can draw conclusions about the status of tomatoes, basil and cannabis. This makes it much easier to assess the needs of the plants.
Systems that work with artificial intelligence and machine learning are particularly good at recognising correlations between a wide range of data. As the network of sensors and machines grows, so agriculture monitoring is driven forward. The Federal Office of Food and Agriculture is therefore promoting the development of AI systems in agricultural contexts. According to the BMEL, the potential ranges from optimising the use of water, pesticides and fertilisers to shortening supply chains by better networking in urban and rural areas.
We can therefore already estimate the potential of consistent monitoring in agriculture very well today. If we set up an "Environmental IoT" in agriculture, as the BfN calls it, we can better recognise connections between agricultural decisions and their impact on the environment. At the same time, this would make it possible to react more sensitively to the requirements of flora and fauna and to monitor the effectiveness of protective measures.
However, the establishment of such a network requires a political framework to protect companies and offer them added value in the acquisition of such technologies. It is also important to make the data accessible to all stakeholders.
" ["post_title"]=> string(73) "Data Instead of Guesswork: The Great Potential of Agricultural Monitoring" ["post_excerpt"]=> string(193) "Using digital methods, researchers from Egypt have succeeded in calculating the soil quality of huge areas with 32 random samples. The project shows how important monitoring is for agriculture." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(62) "data-instead-of-guesswork-on-the-great-potential-of-monitoring" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2024-05-08 11:56:43" ["post_modified_gmt"]=> string(19) "2024-05-08 09:56:43" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=112396" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } [8]=> object(WP_Post)#7060 (24) { ["ID"]=> int(111186) ["post_author"]=> string(5) "21506" ["post_date"]=> string(19) "2024-02-06 08:00:00" ["post_date_gmt"]=> string(19) "2024-02-06 06:00:00" ["post_content"]=> string(18165) "We have been seeing harvesting robots in the fields for several decades now. However, the existing models fertilise, pick and weed ripe crops with a metaphorical mallet rather than a velvet glove. Conventional agricultural robots have therefore so far been suitable for robust foods such as potatoes, cereals or beetroot, but not for produce that requires a lighter touch. However, pilot projects show that modern robotics will change this in the coming years.
The latest generation of robots uses sensors, cameras and AI software to carefully separate only ripe fruit from the plant. The picking robot from Floating Robotics manages to harvest tomatoes, for example, without damaging them in the process. At the same time, it has enough power to unfold fruit crates and transport them away for further processing. Its British brother from Fieldwork Robotics can even pick raspberries, which is a challenge even for humans.
The use of mechanical helpers in greenhouses and fields can reduce both the carbon footprint of agriculture and the use of pesticides. However, researchers also want to get prepared, in the worst-case scenario, global warming cannot be stopped sufficiently.
To understand why conventional agricultural vehicles are too coarse-engined for raspberries and tomatoes, we first need to look at our fields. A harvesting machine for potatoes is called a potato harvester. It tosses potatoes into a collecting device after ploughing the field. Modern versions can then immediately remove plant residues from the potatoes and then clean them thoroughly. Cameras and sensors are already being used here too.
Harvesting fields in this way has been possible with agricultural machinery for decades. Their development has gone from initial, purely mechanical harvesting tools to fully computerised machines. However, these machines have not yet been able to handle plants with enough sensitivity to not damage many in during harvest, or indeed to check whether they are even ready for harvest in the first place.
The harvesting robot from a subsidiary of ETH Zurich, on the other hand, processes data from a camera image with the assistance of AI. It detects differences in colour to identify which fruits are already ripe for harvesting. At the same time, it identifies the position of the tomatoes precisely enough to cut off the fruit just above the stem. The picking robot therefore not only protects the fruit, but also ensures the overall health of the plant. The robot then places the picked tomatoes in a collection basket and moves on to the next bush, all without the assistance of humans.
Floating Robotics would also like to use their machines in the fields. This should make the robots suitable for use in the open air as well as for vertical farming.
Floating Robotics places its picking robots on rails in greenhouses. There, the robotic arm, including the collection basket and required electronics, moves from plant to plant and removes the ripe fruit. The identification of ripe fruit and subsequent picking currently takes ten seconds for tomatoes. According to an information page on Floating Robotics, the process could be shortened to eight seconds for cherries. A faster system would result in the plants being damaged.
At the same time, the robot arm is strong enough to open plant crates. Human labourers therefore only have to take over the removal and quality assurance of the fruit and regularly provide empty crates for the robot.
In the field, the picking robot switches from the rail to tensioned ropes. The system is very reminiscent of the so-called "spider cams" familiar from football stadiums. Floating Robotics erects masts around a field and then attaches the hardware to steel cables between them. The robot is then free to move in three dimensions and is designed to bring the robot safely and precisely to the required location.
In addition to this solution, modern agricultural robotics research is also producing unusual solutions. The autonomous robot from Tevel Aerobotics Technologies relies on wired drones that can pick apples using suction cups. Although their propellers are protected by grids, the construction seems a little more fragile than cable technology.
But whether via drones or on a tightrope, using robots in the field could be a huge advantage.
Thanks to various attachments, harvesting robots can take on other tasks in agriculture. Their cameras already recognise diseases in plants and could instruct the robot to isolate them from other offshoots or treat them in a targeted manner. The use of pesticides should also be much more targeted than before thanks to the precise machines. Instead of spraying fields with pesticides across the board, nozzles spray them directly onto leaves or inject them into the plants' growing medium.
This should make agriculture more sustainable in the future. According to the Federal Environment Agency, the widespread use of pesticides harbours the risk of them being washed away or drifting into nearby bodies of water. In addition, the intensive use of herbicides leads to a depletion of the plant world and deprives many deep-sea species of their food source. Furthermore, studies have shown that "pesticides are one of the main causes of the decline of various species of farmland birds, such as the skylark, yellowhammer and grey partridge, via the food chain".
In Central Europe, we have repeatedly measured record temperatures in the summer months in recent years. In addition, extreme weather phenomena such as droughts and floods are on the increase. The effects of global warming are even more noticeable in countries in the Global South. For farmers and field labourers, this makes work increasingly unbearable and sometimes even life-threatening.
Solar-powered agricultural robots such as the FD 20 from FarmDroid, on the other hand, benefit from direct sunlight. Thanks to an integrated battery that lasts overnight, farmers can use them around the clock for field work. They can conveniently control the robots themselves from their smartphone and only need to go to the field themselves in the event of a breakdown or problem. The same applies to the precise picking robots in greenhouses.
High temperatures are particularly stressful in greenhouses. While a hot, humid climate provides optimum growing conditions for various plants, the work here is particularly strenuous for people. Physical labour in such conditions is strenuous and can only be performed with sufficient breaks and short working hours. However, agricultural robots can work efficiently in hot environments - in principle even around the clock.
Advancing automation in agriculture also prevents another problem: The shortage of skilled labour, which will pose challenges for many sectors of the economy in the coming decades. This is mainly due to demographic developments, the effects of which will be exacerbated by rising temperatures. While there will be more and more old people, this population group will suffer particularly badly from high temperatures.
Even though developments in agricultural robotics are progressing rapidly, there are still some problems that need to be solved in the future. Probably the most serious is one that comes up in every discussion about automated vehicles.
Who takes responsibility if, for example, a field worker is injured by an autonomous field robot? This ethical question, which has been repeatedly discussed in recent years in connection with self-driving cars, will also arise in agriculture in the future.
Farmers who already work with such vehicles also criticise the frequent failures due to the poor coverage of the mobile phone network. Both mobile phone reception and GPS as a positioning system are also restricted by trees or buildings. However, these technologies mean that agricultural robots are primarily dependent on a digital infrastructure in the area of use. And this is not the case in many countries that are particularly affected by the consequences of climate change.
In a way, plants and agricultural robots have one thing in common: they benefit from the controlled conditions that can be created in greenhouses and vertical farms. As experts see them as a useful way of ensuring harvests even in areas with extreme weather conditions, the new developments in robotics are essential.
They could make the operation of greenhouses almost completely autonomous in the future. With increased efficiency, they could produce food in urban centres and represent an important tool in the fight against food shortages. After all, fields require a lot of space and are much harder to protect against environmental influences such as heat and flooding.
However, robot-assisted agriculture also comes with another risk. This is because the developments we have mentioned in this article usually cost five or even six-figure sums. The increasing use of agricultural robots is therefore also accompanied by the risk of increasing inequalities.
Therefore, substantial subsidies, grants and programmes are needed to prevent only rich companies from being able to produce food in the future.
" ["post_title"]=> string(83) "The Rise of Precision Agricultural Robots Paving the Way for Delicate Crop Handling" ["post_excerpt"]=> string(154) "Modern agricultural robots pick even delicate fruits such as strawberries or tomatoes. They are could be set to solve several big problems in agriculture." ["post_status"]=> string(7) "publish" ["comment_status"]=> string(6) "closed" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(83) "the-rise-of-precision-agricultural-robots-paving-the-way-for-delicate-crop-handling" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2024-05-08 11:55:24" ["post_modified_gmt"]=> string(19) "2024-05-08 09:55:24" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(27) "https://reset.org/?p=111186" ["menu_order"]=> int(0) ["post_type"]=> string(4) "post" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } } ["post_count"]=> int(9) ["current_post"]=> int(-1) ["before_loop"]=> bool(true) ["in_the_loop"]=> bool(false) ["post"]=> object(WP_Post)#6718 (24) { ["ID"]=> int(45229) ["post_author"]=> string(3) "197" ["post_date"]=> string(19) "2024-07-31 11:54:39" ["post_date_gmt"]=> string(19) "2024-07-31 09:54:39" ["post_content"]=> string(23588) "Artificial intelligence has long since solved complex tasks and made our everyday lives easier. But do intelligent computer programs also provide new solutions for environmental and climate protection?
Unnoticed by many, artificial intelligence (AI) has long been intervening in our everyday lives—be it as a translation system, search engine, personal language assistant or robots programmed for specific activities. "The fact that AI will be an enormously important criterion for success in the future for science, society and especially the economy, is now sufficiently accepted," says Kate Saslow, project manager for AI and foreign policy at the German Stiftung Neue Verantwortung (New Responsibility Foundation). This is largely to do with the fact that the potential applications of intelligent computer programs are extremely versatile and, as a cross-cutting technology, connect diverse areas.
What has become clear is that AI is likely here to stay, and will only develop further in the future. Instead of being stuck debating the question of whether we need or want AI—and thus missing the chance to have a say in its development—we should be discussing how and where we can use AI to serve the general good of humanity.
Voice or face recognition, search engines, self-driving cars, computer games, social bots (chatbots that mimic human communication) or robots—all of these areas heavily involve AI.
But what exactly is artificial intelligence? So far, there is no single universally valid definition of AI. If we look for the lowest common denominator, artificial intelligence can be described as computer programs that can learn and improve themselves. They are intelligent to the extent that human cognitive decision-making structures are replicated by programming. This includes visual perception, speech recognition and generation, reasoning, decision-making and action, as well as the ability to adapt to changing environments.
Simple, "non-intelligent" algorithms (for example, navigation devices) are programmed to answer clear questions: this is the problem and this is how it is solved (e.g. "find the shortest way from A to B"). The very heterogeneous AI applications, on the other hand, are not based on classical deterministic programming, but on statistical data analysis. In a learning process—also called machine learning—the artificial system learns from examples based on training data, which it can generalise independently after the learning phase. An AI-supported translation system, for example, is fed hundreds of thousands of data in the training phase. It does not learn the examples by heart but focuses on patterns and regularities. In this case, it learns which word is translated into which word and which sentence into which sentence, which can then also be applied to new, previously unseen data.
The European market for artificial intelligence is predicted to grow 35 percent in 2024, according to Statista. The decisive factor for the rapid developments in the field of AI applications in recent years is the fast-growing computer power. This makes it possible to process the large amounts of data of rapidly advancing digitalisation. An end to these developments is not in sight. Computer scientists Dario Amodei and Danny Hernandez, who both work for OpenAI, a non-profit research company founded by, among others, Elon Musk, conclude in their study that an increase by a factor of 300,000 took place in the period from 2012 to 2017. This means that the new AI systems are currently, on average, doubling their speed every three and a half months and are thus able to process ever larger amounts of data.
AI is being treated as a key technology in many social, and especially economic, areas. Could it also transform our society towards ecological and social sustainability? In view of the urgent need for action to avert or at least contain climate collapse, a wide variety of projects, start-ups and research projects have set out to find new solutions with the help of intelligent computer programs.
In earth observation, for example, machine learning methods (such as pattern recognition and the linking of large amounts of data) together with data collected by satellites can help to make precise statements about climatic change.
In the energy sector, AI can help to regulate the growing complexity of the decentralisation of the energy system which goes hand in hand with the switch to renewable energy sources. It also helps us use infrastructures more efficiently and increase the flexibility of the energy system by intelligently networking energy systems (smart grids), intelligent building control and energy data management.
AI applications could also contribute to making the transport system more environmentally friendly, as well as assist in waste and recycling management.
For more examples of how AI is helping to protect wildlife and preserve our forests, click here: Sustainability and AI.
The topic of AI brings up several ethical dilemmas. Key questions include:
Autonomy: How autonomously does an AI act? The concept of autonomy of AI systems calls into question the self-determination of humans. This is a particularly key debate in the case of autonomous vehicles.
Trustworthiness and Transparency: How does an AI reach a decision? There is a certain lack of transparency in the principles of machine learning algorithms since the use of statistical correlations makes the algorithms more difficult to understand. For example, it is not always possible to deduce from an algorithm why a certain input value produces a certain output value. The reasoning that "the algorithm learned it that way from the sample data" is unsatisfactory for a deeper understanding. This can also become a problem from an environmental perspective; namely, when decisions made by AI systems cause environmental damage.
AI systems must be prevented from reproducing the discriminatory patterns that exist in society and from making unsustainable decisions. This can be done by ensuring algorithms are transparent and that the training data for algorithms is designed more inclusively.
Most start-ups, financial service providers and established e-commerce companies have long since integrated AI into their mobile applications, shopping systems or the data processing of their online stores. The intelligent algorithms classify and predict, suggest products to customers and make purchase recommendations.
What is worrying is that the dominance of the large IT and internet corporations (Google, Microsoft, Apple, Facebook, IBM, Amazon, etc.) is continuing in the AI sector, as in all other areas of the digital economy. These globally active providers of digital services now have a near-monopoly position and have huge pools of data at their disposal. From a sustainability perspective, the main problem is that digital corporations are using AI applications to further personalise their services. They can then create increasingly accurate forecasts about what customers want to buy, leading to more consumption. The German Federal Environment Agency study, “Consumption 4.0” states: "The lowering of technical barriers, the integration of purchase recommendations and evaluations into consumers' everyday lives via social networks and personalised marketing can encourage consumers to make more frequent purchases, limited only by their disposable income."
In addition to the discourse on the ethical risks of AI, the question of the life cycle assessment of AI systems is also relevant from a sustainability perspective. While some see enormous opportunities in the fact that we can tackle environmental challenges with AI, others fear it will merely create new problems. These include the further increase in our already enormous demand for electricity and massive increases in consumption.
The concerns are not unfounded: AI applications such as deep learning, simulations and forecasts require more and more computing power and will increase energy demand in data centres. As researchers at the University of Massachusetts have determined, training a single AI application for speech recognition can generate five times as much CO2 as a car emits over its entire lifetime.
AI models are often made up of Graphical Processing Units (GPUs) which are located on servers in data centres. Keeping data centres cool is a huge challenge that requires both energy and clean water, increasing the environmental impact of these servers and the AI models that use them. GPUs are also made from metals and minerals that require a lot of energy and processing to procure.
However, the possibility of AI models could lead to emission reductions in the long term. The emissions output of training AI should be weighed up against potential societal gains on a case-by-case basis.
Future AI applications could also have lower energy consumption, as there are still refinements that can be made, especially in the area of machine learning. "Many research groups are working on making deep learning less energy-hungry. Different hardware, new models and learning methods—all this is being investigated," says Prof. Dr Kristian Kersting, Head of the Machine Learning Department at the Darmstadt Technical University.
In December 2018, Germany published the German AI Strategy, which referenced sustainability concerns and opportunities of AI. The strategy outlined how evaluation criteria for the environmental impact of AI should be developed and an environmental data cloud should be established.
The plan to reach these goals included a specific funding program, "AI Lighthouses for the Environment, Climate, Nature and Resources", in which the Federal Ministry for the Environment (BMU) looks for projects from business, science and civil society that use artificial intelligence to tackle ecological challenges. 35 projects were approved during the first funding program and 46 million EUR was allocated. Projects included Nadiki, an AI system that aids resource management and reduces CO2 emissions, and DC2heat, a digital twin that optimises the use of waste heat from data centres. Since 2018, the AI strategy has been updated and more funding has been allocated to the promotion of AI, with 5 billion EUR now allocated by 2025.
In March 2024, the European Parliament published the Artificial Intelligence Act. These guidelines have been in discussion since 2021 and focus on outlining regulations for the development of artificial intelligence. The 500-page publication recognises the potential benefits of AI for climate protection while acknowledging the need to reduce the impact of AI on environmental sustainability.
The intersection of AI and climate change is being addressed worldwide by institutions, universities and researchers alike. In August 2024, the United Nations published an impact assessment on harnessing the power of AI for climate change. Increased amounts of funding are available for projects that focus on AI in the environmental field, from the BMUV in Germany to Horizon in Europe.
With this overview, we have set out to discover how far AI applications can contribute to environmental and climate protection. Two things have become clear. As initial projects show, AI applications certainly have the potential to support sustainable development by contributing to more efficient use of resources, providing us with accurate information about the state of our planet or making forecasts easier. But assuming AI leads to a more sustainable planet is not a foregone conclusion. There are ethical concerns, electricity consumption and the dominance of corporations (which could lead to growing consumerism) to consider.
For AI applications to contribute to environmental and climate protection, suitable framework conditions must be created. This includes, for example, guidelines that keep energy consumption within limits and a political framework that guarantees data protection and transparency. This also includes ensuring that data and AI systems are not controlled by just a few global players.
To drive innovative, sustainable AI developments, it's important to promote research and young start-ups while creating new interfaces between research communities. Currently, research communities that deal with AI and those that deal with climate and environmental problems work together far too little.
To the question of whether AI can save the world, the answer likely remains, no. AI technology alone will not save the world; other technologies won't save the world either. But artificial intelligence can make an important contribution—if we use its power for good.
This article was originally published in December 2019. It was updated in April 2022 by Marharyta Biriukova and in August 2024 by Kezia Rice.
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© Artificial Intelligence: Can We Save Our Planet With Computing Power?
Digitalisation Can Advance Sustainable Agriculture – Under Certain Conditions
© AI-Model Aardvark Forecasts Weather Without the Need for Carbon-Emitting Supercomputers
Hacker Congress 38C3: All About Sustainability in the Digital World
“The First Location Is a Stumbling Block or a Stepping Stone”: How AI Model GeoMatch Is Relocating Refugees
© Identify Plants With the Flora Incognita App – And Support Important Biological Research
© Why a Digital Twin of the Arctic Has “The Potential to be Game-Changing”
© Data Instead of Guesswork: The Great Potential of Agricultural Monitoring
© The Rise of Precision Agricultural Robots Paving the Way for Delicate Crop Handling
