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[前沿资讯 ] Two Wageningen AI studies featured at prestigious AAAI conference 进入全文
Wageningen University & Research；
Two Wageningen University AI studies will have a platform during one of the world’s top conferences on AI. In the first study, reinforcement learning was used to provide a cost-effective solution for farmers to determine when to fertilise their crops. The second study contributed to a hybrid model that maps the effects of temperature increase on the phenology of cherry trees. The bar is set high during the AAAI Conference on Artificial Intelligence, which will be held in Philadelphia, from February 25 to March 4. The organisation accepted only a small fraction of the scientific articles submitted. Two of these come from the new AI chair group at Wageningen University & Research. Professor Ioannis Athanasiadis is quite proud of this: “AI is developing extremely fast. The fact that these two studies are accepted at this selective conference shows that WUR can bring important expertise to the global discussions on AI development.” Fertilise or don’t fertilise The first study focused on the question of whether AI can help farmers decide to fertilise their land – or not. Senior AI researcher Michiel Kallenberg explains: “One of the consequences of over-fertilisation is that nitrogen that is not absorbed by plants ends up in the environment. To prevent over-fertilisation, farmers can run tests to determine how much fertiliser a plant needs. AI can then incorporate this knowledge into a farmer’s fertilisation schedule. The problem is that it is quite time-consuming and costly to get these tests done. This is why many farmers make decisions based on common sense. Or they simply fertilise the maximum amount allowed.” Reinforcement learning Kallenberg and his colleagues worked on an AI agent that determines the correct amount of fertiliser to be applied, while minimising the need for expensive field assessments. An existing crop model for winter wheat, developed by Wageningen scientists, forms the basis for this. “This model reflects the growth of the crop based on all kinds of variables, such as weather data and soil information. Then, with the help of the AI agent, we applied a large number of fertilisation strategies in a simulated environment. We used the technology of reinforcement learning for this. By learning through trial and error, the algorithm determines the optimal fertilisation strategy for a diverse range of conditions” Next step: apple trees Until now, the experiments have been conducted with computer simulations. Next, the researchers will test the AI agent in an upcoming field trial. An exciting step, as field conditions may present factors not encountered in simulations. “In addition, we have trained another AI agent following a similar principle to optimise pesticide application”, Kallenberg says. “This agent will be tested in an apple orchard, where the primary focus is pesticide use and disease management.” Computer simulations and nature According to Athanasiadis, the study convincingly demonstrates how you can use AI to explore the potential of nature. “That is what the AI agent does: by connecting computer simulations with nature, we find new solutions to use fertiliser more effectively while maintaining high yields. This study shows how good AI can help us manage risks related to climate change and prepare for the future in simulated environments” Cherry trees He believes that what characterises the study is that the needs of the sector were the guiding principle. The same applies to the other study by his research group that was selected by the AAAI. It revolves around cherry trees. “In countries such as Japan, South Korea and the US, cherry tree blossom is of great cultural value. In addition, there is a tremendous amount of knowledge about the phenology of these trees. This knowledge goes back thousands of years.” Blossoming earlier and earlier The cherry tree has traditionally marked the beginning of spring. Due to climate change, the blossoming period is occurring earlier and earlier in the year. Athanasiadis: “During the dormant period, a tree stops growing and stores this energy until a certain threshold is reached. Then the tree uses the stored energy to blossom. We see that the blossoming is happening earlier and earlier. But we cannot predict when it will happen because we simply cannot measure it physiologically. It is a hidden process.” Machine learning To get a look at this hidden process, an AI model was developed, powered by machine learning: “First of all, we conducted detailed research into various scenarios. We then linked existing biophysical process models to a neural AI network that we developed. We tested the operation of our hybrid model in an extensive case study in Japan, South Korea, and Switzerland.” Better predictions The results were surprising, says Athanasiadis: “The combination of biophysical process models and machine learning yielded better predictions than when the separate process models or AI models were used. It shows how great the added value can be when you link domain knowledge to the advantages of AI. By combining the best of both worlds, we can better understand how plants work.” Reliable systems needed He emphasises that there is still a lot of work to be done to translate these kinds of fundamental insights into AI solutions that can be used in practice. “The agri-food sector needs reliable systems for this. As Wageningen scientists, we can make a difference for the sector in this regard. AI is the talk of the town, and as Wageningen we must participate in a responsible way: We not only ensure that AI tools work, but we base them on the needs of the sector and the extensive knowledge of plants, animals, food and the environment at WUR, in order to translate them into targeted algorithms.” WUR is also making great strides with AI in other studies. For example, scientists in Wageningen recently developed a machine that uses AI to recognise and remove weeds. Other recent inventions include a robot that cuts off ripe fruit and a greenhouse that autonomously regulates its own climate.
[前沿资讯 ] Artificial Intelligence for Sustainable Futures 进入全文
Wageningen University & Research；
On Friday 7 March 2025, Wageningen University & Research will celebrate its Dies Natalis. This year's theme is: Artificial Intelligence for Sustainable Futures. 7 March 2025 | 15:00-17:00hrs Wageningen Campus, Orion Building, Bronland 1, Wageningen or online During our Dies Natalis celebration, we will explore how scientists and students can become responsible change makers and leverage AI to envision a future where a sustainable world becomes a reality. Artificial Intelligence holds the potential to contribute immensely to science by data analysis, accelerating discoveries, and enabling innovative research and education methodologies. From optimising agricultural practices to estimating global change impacts, AI can be a powerful tool driving scientific innovation and sustainability worldwide. However, as we explore its transformative promise, we must also address the challenges that come with its use in scientific discovery. Researchers and students have an important role to play in ensuring that Artificial Intelligence is used responsibly and effectively. The official Dies Natalis celebration will be preceded by the pitches for the Research Award, organised by Wageningen Graduate Schools and made possible with support of University Fund Wageningen. For more information on the programme of the Research Award, see the Research Award webpage. Programme official celebration Dies Natalis 14:00 Welcoming reception and registration 15:00 Artificial Intelligence for Sustainable Futures Introduction by Prof.dr Carolien Kroeze - Rector Magnificus and Vice-President of Wageningen University & Research Dies lecture by Prof. Andrea E. Rizzoli - Director of IDSIA USI-SUPSI and (full) professor at SUPSI (University of Applied Sciences of Southern Switzerland) Intermezzo Presentations by early career scientists Hilmy Baja, Smaranda Filip and Paulan Korenhof Research Award Closing words by Rector Magnificus Prof.dr Carolien Kroeze 17:00 Drinks Practical information Venue: Wageningen Campus, Orion Building, Bronland 1, Wageningen Register: please use the button below. Please, register before 4 March 2025. Information: if you have any questions, please contact us at dies@wur.nl. Prof. Andrea E. Rizzoli: AI-research for science in Europe is outstanding Andrea Emilio Rizzoli is a distinguished researcher and professor with dual Swiss and Italian citizenship. Currently, he is the director of the Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA USI-SUPSI), a professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and adjunct professor at the Faculty of Informatics of Università della Svizzera Italiana. His research interests include decision support systems, modelling and simulation of dynamic systems, and the application of operations research and artificial intelligence techniques to natural resource management. Throughout his career, Rizzoli has authored over 200 publications and has been recognised with several awards, including the Early Career Research Excellence award by the Modelling and Simulation Society of Australia and New Zealand. Improving AI In his keynote, Prof. Andrea E. Rizzoli will highlight how AI can foster scientific progress and what challenges the use of AI in scientific discovery poses. He will also address what European researchers can do to improve the way AI is used in the scientific context as well as in society at large. The impact of AI on scientific progress is huge, according to Rizzoli: “Just think of the Nobel Prize for Chemistry awarded to Baker and Hassabis, the latter a co-founder of DeepMind, an AI company. Yet, I do not think that the progress we observe is due to the rapid development of AI. On the contrary, the current impact of AI on scientific research is the result of decades of research. We are just reaping the fruits. At the same time, the recent developments of Generative AI are already producing promising results, and there’s for sure more to come.” The cost-benefit ratio AI and sustainability is a very hot topic, Rizzoli acknowledges. “On one side we see the impact of AI-enhanced research which can make better use of data at scale, increasing, for instance, the resolution of climate change models, or solving complex optimal management problems by means of reinforcement learning, or producing better forecasts of extreme events. On the other side, there are the increasing energy costs of training huge AI models, and the cost-benefit ratio is still uncertain.” AI-research for science in Europe is outstanding, he emphasizes. “For instance, there could have not been AlphaFold, the AI tool that won the Nobel Prize for Deepmind, without the data collected and provided by the European Molecular Biology Laboratory. Yet investments in research are lagging, and despite the promising efforts of the various funding programmes of the EU, the working conditions to attract top-tier researchers in European universities are still not there. We have a unique opportunity to attract AI talent in Europe, given the current geopolitical situation, and yet our Universities are not yet prepared.”
[前沿资讯 ] Yamaha Motor Launches Yamaha Agriculture to Deliver Automation and Digital Crop Management Solutions 进入全文
Yamaha Agriculture；Global Ag Tech Initiative；
Yamaha Motor Co., Ltd. recently announced the launch of Yamaha Agriculture, Inc., a new company focused on delivering autonomous equipment and AI-powered digital solutions that help growers in the specialty crop market become more sustainable, profitable and resilient in the face of scarcer resources and climate change. Through the strategic acquisitions of Robotics Plus and The Yield, Yamaha Agriculture will provide robotics solutions for spraying, weeding, and other field operations, while leveraging advanced data analytics and AI to enable precision farming and data-driven decision-making for growers of wine grapes, apples, and other specialty crops in North America, Australia, and New Zealand. “Establishing Yamaha Agriculture is a pivotal milestone in our Long-Term Vision 2030, ART for Human Possibilities. This initiative embodies the three core pillars we aim to achieve in our 2030 Long-Term Vision: Advancing Robotics, Rethinking Solutions and Transforming Mobility,” said Jim Aota, Chief Strategy Officer for Yamaha Motor. “It also aligns with Yamaha’s global technology roadmap, focusing on advanced energy management, intelligent systems, and software-driven solutions. With the launch of this new company, we aim to leverage Yamaha’s technological expertise to contribute to sustainable and profitable farming using a customer-centric approach. Growers will be able to better address challenges around labor shortages, resource scarcity, and impacts from climate change.” Autonomous Technology Joins Forces With Data-Driven Decision Making Robotics Plus provides an autonomous hybrid vehicle capable of multiple activities including spraying and weed control, addressing key labor challenges faced by growers. The Yield brings advanced data analytics and AI-powered models to deliver yield predictions and optimize on and off-farm operations. Leveraging Yamaha’s long heritage as a trusted manufacturer of high-performance products, the new agriculture business will scale these two innovative solutions with a focus on quality, reliability, and safety. These complementary technologies will be integrated to create a comprehensive platform that enables precision farming for growers. By combining autonomous equipment with intelligent data insights, Yamaha Agriculture helps growers reduce input costs, optimize resource utilization, and improve overall farm productivity and sustainability. “Guided by our mission to ensure growers are sustainable, profitable and resilient, Yamaha Agriculture recognizes that the challenges facing specialty crop growers require thoughtful solutions that will take time to develop,” said Nolan Paul, Group CEO of Yamaha Agriculture, Inc. “We believe meaningful innovation in agriculture emerges through close collaboration with growers and industry partners. The capabilities of Robotics Plus in robotics and automation and The Yield in AI-powered analytics represent two important building blocks in addressing these challenges. As we work to bring these technologies together, we are committed to a deliberate approach that prioritizes creating real value for growers while maintaining the high standards of quality and reliability for which Yamaha is known.” A Modern Approach to Agricultural Innovation Recent research reveals a decisive shift toward automation, robotics and digital adoption across the agricultural sector. As stated in the Robotics-Ready Data Standards for Washington Apples report from The Washington Tree Fruit Research Commission (WTFRC): Growers believe that automation could support many functions of their operations, including harvesting and sorting (76%), autonomous spraying and fertilizing (52%), and both crop load and yield forecasting (29%). A large majority – 86% of respondents – say digital technologies like software and robotics have already, or will in the next 5-10 years, drastically change agriculture as we know it. In addition, specialty crops require a higher reliance on labor than row crops: According to the USDA, specialty crops have the highest labor costs across farm types at 38 cents of every dollar in cash expenses in contrast to 4 cents of every dollar for corn and soybean operations. Specialty crop growers are now spending an average of $500,000 USD a year on automation in response to the persistent ag labor shortage, according to the 2022 Western Growers Specialty Crop Automation Report. Building on Nearly 40 Years of Agricultural Automation Innovation Yamaha’s journey in agricultural automation began nearly 40 years ago with the development of unmanned helicopter technology, making it possible to reach terrain inaccessible to conventional tractors and ground equipment. In Japan’s challenging rice paddy fields, for example, more than 2,200 units now cover 800,000 hectares annually. Beyond rice fields, these versatile machines are also used globally for applications such as managing wine grapes, invasive weeds, tree fruit, and sugar cane. The establishment of Yamaha Agriculture is a natural evolution of this pioneering work in automating challenging environments. The agricultural sector is experiencing a remarkable technological inflection point, with industry leaders enthusiastically embracing digital transformation through autonomous systems, robotics, and intelligent systems, including AI-powered precision analytics. Yamaha Agriculture is launching at a pivotal moment and is well-positioned to support the industry’s strong appetite for innovative AgTech solutions that combine advanced automation with powerful data-driven insights.
[前沿资讯 ] Fewer humans, more AI: Is this the future of the agricultural and horticultural sectors? 进入全文
Wageningen University & Research；
A machine that recognises and removes weeds, a robot that cuts and picks ripe fruit with arm grippers or a greenhouse that autonomously controls the climate. This is all in a day’s work for WUR researchers Bram Veldhuisen and Anja Dieleman, who are involved in the development and implementation of these smart AI technologies. This makes the agricultural and horticultural sectors more sustainable, improves their yields and makes them less labour-intensive. We asked them about promising solutions, the hurdles to overcome and the potential risks. Artificial Intelligence (AI) offers solutions for agriculture and horticulture in several areas, says Bram Veldhuisen, researcher in precision agriculture and agri-robotics. “Like recognising weeds or harvesting crops. You can train a machine to distinguish between a potato plant and weeds, for example, or to know when a crop is ripe and ready to be harvested. This is obviously very convenient because doing all that manually - as is mostly the case now - is extremely time and energy consuming. Moreover, it’s easy to overlook things with the naked eye. Smart systems equipped with high-tech cameras and sensors can detect more.” Smart implements One of the challenges is how to ensure that these smart systems are also - almost - 100 percent reliable. Smart implements are the missing link here, says Veldhuisen. “This is an integrated system in a machine or robot that has the ability to check its own work. If you send a robot into the field to clear weeds, you don't want it to accidentally take some of the crops too. And if you want to harvest, then you only want to remove the ripe crops from the field. A smart implement knows whether the work is going well or whether someone needs to intervene or make an adjustment.” Signal if something goes wrong Smart implements are not yet applied in practice, says Veldhuisen. “At WUR, we are working on developing this, including in Robs4Crops. In this project, we developed a camera system for a hoeing machine in a beet field. This allows the machine to look ahead and behind, counting the number of crop plants coming up and behind it. If there’s a mismatch between these numbers, the machine gives a signal, so the farmer knows something has gone wrong. You could also develop such a system for the sowing process. For every type of machine, it is therefore possible to devise control steps that are currently still carried out by humans.” Smart harvesting and picking robots In other projects, WUR researchers are working on another application of AI technology, namely smart harvesting robots. Veldhuisen: “A few years ago, we developed a harvesting robot for broccoli. We fitted the robot with a camera system that sees very precisely which broccoli is ripe enough. Similar applications have been developed in other crops, such as a harvesting robot for peppers. WUR is also working on another project, The Next Fruit 4.0, which focuses on fruit growing. This also looks at picking robots. Developing these is slightly more challenging because you need to be even more careful with fruit than with other crops. When picking an apple or pear, it should be twisted rather than pulled or cut. You also want to avoid any bruising. Autonomous cultivation in greenhouse horticulture AI is not only playing an increasingly large role in arable farming but also in protected cultivation, like greenhouse horticulture. Particularly when it comes to autonomous cultivation, says Anja Dieleman, researcher in the field of plant physiology. “Autonomous cultivation is about remotely controlling cultivation in a greenhouse. This is done using information from sensors and cameras relating to greenhouse climate, plant development or the presence of diseases and pests, for example. With the development of smart models, growers can use this information to make decisions. Should the temperature be higher or lower? Do I need to give extra water? When can I harvest? Do I need to use crop protection products?” Intelligent algorithms and digital twins Dieleman is project leader of AGROS, in which WUR is working with a consortium of companies to develop an autonomous greenhouse. “We have now reached part 2 of the project, AGROS II. In AGROS I, the work included developing good vision technology in the greenhouse. In part 2, this will be elaborated further, including a mobile camera system that moves through the greenhouse. This enables us to measure the development of a large number of plants, allowing us to control cultivation more effectively. “We do the control using intelligent algorithms and digital twins based on mechanistic models. This is how we make autonomous cultivation even smarter.” Level is getting higher However, we are not yet at the point where we can control a greenhouse completely autonomously, says Dieleman. “We are now at the level where data and intelligent algorithms mainly support growers in their decisions. Particularly in the field of autonomous climate control, the sector is already quite advanced. Controlling autonomous crop management is trickier. For example, determining the right time to irrigate, space plants and harvest ripe fruits. That requires crop feedback. And that’s where the challenge lies. Although there are good sensors on the market for continuously measuring greenhouse air temperature, for example, there are none that automatically determine the daily number of newly formed leaves. We are now developing vision technology for that. But even if we can’t control a greenhouse completely autonomously, these developments help to improve greenhouse cultivation. Because we are working on this, growers are becoming more aware of the importance of data collection and cultivation plans. This will automatically raise the level of cultivation.” Practice stands to gain Both Dieleman and Veldhuisen see that farmers and growers are eager to make more use of smart technology. Veldhuisen: “Not just because it improves their yields, but also because of the issues around labour supply. Fewer and fewer people want to do intensive work in the field. Smart machines offer a solution to that problem. We are also seeing an increasing awareness of issues such as climate and the environment. By making smarter use of energy, water and nutrients and replacing chemical control with more environmentally friendly methods, such as laser weeding, operations have a less harmful impact. Now it's just a matter of ensuring technology maturity and practical accessibility.” More autonomy not without risks Finally, experts emphasise that introducing more autonomous technology is not without risks. Dieleman: “If you want to control a crop remotely, you need to be able to rely on the data. If a hose is loose or a well is blocked, the numbers are no longer correct. To avoid such problems, you need to incorporate an extra check.” Veldhuisen: “Another issue that arises when you start working more digitally is the risk of cybersecurity threats. If someone with the wrong intentions hacks your system, they could set the machine to destroy all your plants. And you need to ensure that autonomous machines move around safely. In that respect, we can follow the development of self-driving cars. But first, let's prove that everything we want now actually works.”
[前沿资讯 ] Future AI might think more like an animal 进入全文
Wageningen University & Research；
For their experiments with artificial intelligence, Dr. Steffen Werner and Dr. Michael Coughlan drew inspiration from the ways animals think. Their insights could help to develop neural networks that are more stable, efficient, and versatile. “Although artificial intelligence is performing impressive feats, Silicon Valley’s big models trained on massive amounts of data have started showing diminishing returns: they are not developing at the pace they used to,” explains Dr. Steffen Werner of the Experimental Zoology Group at Wageningen University & Research. “On top of that, these large models are quite opaque. It’s difficult to understand how they function, and thus, how they could be improved.” Werner and his colleague Dr. Michael Coughlan went back to basics, experimenting with the smallest possible deep learning algorithms. “As physicists, we strive to understand through simplification, looking for the minimal system,” explains Coughlan. For their experiments, the scientists drew inspiration from the animal world. “Animal brains have aspects that would be the envy of many artificial models. They can function while they’re still growing, they require less extensive networks, and they can learn more than one task at the same time,” says Werner. “We replicated those qualities in small neural networks, both to see if it was possible and to study them.” The insights gleaned from these models point into new directions for the development of artificial intelligence. Discovering surprising qualities “One hurdle artificial intelligence has faced is called catastrophic forgetting,” explains Werner. “Say you’ve trained an AI to recognize handwriting, and now you’re teaching it to recognise shapes. Often, what will happen is that it will forget the first task while learning the second one. In contrast, a lot biological minds can learn several things at once, without ever risking this overwrite.” The scientists succeeded in teaching their model two things at once by continuously alternating between the two tasks during its training period. “Although training does take a little longer, the AI learned to recognise both handwriting and shapes to about the same degree of success as its specialised counterparts. What was interesting, though, was to see that the neural network had partitioned itself into roughly two parts. It used each half for a separate task. We hadn’t built it that way, this structure emerged from the training,” says Werner. Werner and Coughlan discovered another surprising quality when they tried to replicate the neurogenesis of animals. “That is the process by which their brains grow. Most important for us was the way biological cognition remains functional all throughout its growing process. So we wanted to know if an artificial intelligence would break down if we expanded the model while it was already in training,” says Coughlan. “It didn’t. In fact, we were surprised to see an increase in the stability of the training process. Dips in accuracy, not unusual during training, became less frequent.” Understanding artificial intelligence The physicists strived, first and foremost, to understand. “These experiments were fueled by our curiosity. We wanted to know more about the similarities, but also the differences, between biological and artificial neural networks,” says Coughlan. The emergent qualities their experiments turned up also beg new questions from the scientists. Werner gives an example. “Does the relative similarity of the two tasks we taught the model relate to the way it partitioned itself into two halves? Would the network look different if the tasks were less similar?” Still, in a field where the dominant approach has come under scrutiny, it’s research like this that might open up new avenues. The construction of ChatGPT and Gemini, for example, is domineered by an engineering approach. “These models are built to work, not to be understood at every level,” explains Werner. “That would’ve slowed down their development immensely.” In contrast, DeepSeek, the Chinese AI that shook up the industry in January 2025, came out of a hedge fund domineered by mathematicians. “Like physicists, they would be interested in what makes AI work the way it does,” says Coughlan. That could explain why DeepSeek performs at such an impressive level, while also functioning more efficiently and costing less to build and train. “AI has shown impressive progress these last few years,” says Werner, “but the recent slump might call for a new approach. Maybe the time has come to slow down and pursue a more scientific understanding of the way artificial intelligence works.”
[前沿资讯 ] ‘AI can help growers discover more about their crops’ 进入全文
Wageningen University & Research；
Growers have access to an increasing amount of data: about their crops, the greenhouse, sales, labour, and more. This data is often scattered across various sources, apps, tools, or websites of other companies. Wageningen University & Research BU Greenhouse Horticulture is exploring how artificial intelligence (AI) can help growers draw connections between this data. Researcher Rick van de Zedde: “By using AI smartly, data can be turned into valuable insights.” A grower has extensive green knowledge and is capable of making cross-connections. For example, higher humidity in the greenhouse reduces plant transpiration but increases the risk of fungal diseases. In this case, the number of variables is limited. However, due to digitalisation in and around greenhouses, the amount of data has grown enormously. Sensors, models, suppliers, artificial intelligence, image recognition, and more now generate terabytes of data. Data-driven cultivation “High-tech greenhouses are becoming increasingly large. This requires a data-driven approach. A grower must be able to schedule the required workforce at a specific time in the growing season, instruct staff to use (natural) resources efficiently, and verify the impact on crop quality and yield forecasts.” The art of data-driven cultivation lies in identifying possible correlations among all these data streams to make well-informed decisions. AI can enable this. However, it requires data ‘providers’ (such as climate computer developers) to grant access to the data and to handle the results responsibly. Van de Zedde: “The data must be shared in a controlled and reliable manner.” Which data to connect? WUR collaborates with various companies to unlock data for AI applications, including Hoogendoorn, LetsGrow.com, Ridder, Hortikey, Eurofins, and Log & Solve. “Together, we look for correlations and explore how combining different sources adds value. To achieve this, we bring together experts with IT backgrounds, crop knowledge, and energy expertise. They know what needs to be measured because AI always starts with human input.” Many questions remain. For example, could a grower instruct a system to look for correlations without being overwhelmed with graphs? Van de Zedde suggests: “The ideal scenario would be to do this in natural language, like ‘ChatGPT.’ A grower wouldn’t need programming skills but could simply ask their question. For instance: ‘Over the past few years, is there a correlation in this compartment between disease pressure and climate?’” Another question is: In what form should AI provide the answer? Van de Zedde notes: “Some growers prefer just a ‘yes’ or ‘no,’ while others want a detailed graph.”
