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[前沿资讯 ] AEM and CEMA Strengthen Cooperation on Advocacy and Regulatory Efforts for the Ag Equipment Industry 进入全文
Association of Equipment Manufacturers；Global Ag Tech Initiative；
The Association of Equipment Manufacturers (AEM) and the European Agricultural Machinery Association (CEMA) have signed a Memorandum of Understanding (MoU) to enhance advocacy efforts for the agricultural equipment industry. The agreement aims to create a positive legislative and regulatory environment across the Atlantic and globally, ensuring alignment on key issues impacting manufacturers in both regions. The MoU establishes a framework for the two organizations to work together toward alignment, achieving mutual recognition, ensuring regulatory compliance, and harmonizing practices and standards in several key areas that impact agricultural equipment manufacturers today. Signed last month at two major industry events—EIMA International in Bologna, Italy, and AEM’s Annual Conference in Palm Springs, California, USA— the agreement formalizes the partnership between CEMA and AEM. “With the signing of this Memorandum of Understanding, AEM and CEMA look forward to enhanced collaboration in order to address key issues of importance and ensure there is alignment between Europe and the U.S. as it relates to our two very important global machinery markets,” said AEM Senior Vice President Curt Blades. The MoU specifies several areas where the two organizations agree to increase cooperation: Engine emissions Autonomous equipment Cybersecurity Machine safety and technical requirements Advanced technology in agriculture Sustainability “This agreement strengthens the transatlantic connection between AEM and CEMA, as we work together to support agricultural equipment manufacturers as they build the products that help feed our world,” said CEMA Secretary General Jelte Wiersma. By focusing on these shared priorities, AEM and CEMA aim to help the industry navigate global markets while advancing innovation, sustainability, and safety standards for the benefit of manufacturers and farmers alike.
[前沿资讯 ] Top 5 AgTech Trends for 2025: What’s Next for Regenerative Agriculture? 进入全文
Ron Baruchi；Global Ag Tech Initiative；
Editor’s note: Looking forward to 2025, AgTech promises to deliver scalable solutions to pressing challenges such as resource scarcity, climate change impacts, and food system resilience. Innovations in regenerative agriculture, AI-powered data insights, biosolutions, and digital twins will lead the way. Reflecting on 2024’s progress, Ron Baruchi, CEO of Agmatix, explores how these advancements shape the future of global agricultural innovation, turning potential into practical, impactful change. This past year has tested the agricultural technology sector and growers alike, with rising costs, climate challenges, and persistent uncertainties pushing many to embrace a “do more with less” mindset under tight deadlines. While AgTech has continued to revolutionize traditional practices, the challenges of the past year have tempered its speed and impact. My 2024 predictions highlighted the sector’s readiness for transformation, but the year’s realities underscored the resilience required to overcome these hurdles. As we reflect on how technologies evolved in 2024 to support these realities, we explore what they mean for the future of agricultural innovation on a global scale — turning potential into practical, scalable solutions to meet the demands of a changing world. #1 Data-Driven Regenerative Agriculture Regenerative agriculture is transforming global farming by improving soil health, biodiversity, and sustainable crop production. AI has empowered farmers to adopt these methods more effectively by tailoring decisions to local conditions and ecological needs. Going into 2024, I anticipated increased adoption of regenerative practices, supported by advanced analytics. Adoption indeed accelerated, but with a broader focus than expected. The McKinsey Global Farmer Insights 2024 survey revealed that 68% of farmers adopted crop rotations, 56% implemented reduced or no tillage, and 40% used variable rate spraying or fertilization. However, motivations varied regionally, yield improvement was key in India, Latin America, and North America, while European farmers prioritized additional revenue streams. Looking Ahead: The “nature positive” movement, which emphasizes net gains in biodiversity and ecosystem health, is gaining traction. 2025 will likely see metrics expand beyond carbon to include soil quality, biodiversity levels, and forest conservation, enabling farmers to meet regional specific ecological requirements. This evolution supports practical ways to measure and assess the impact of regenerative agriculture, thereby enhancing the resilience and sustainability of agricultural systems. #2 Generative AI: Unlocking New Data Potential Generative AI is revolutionizing agriculture by transforming vast datasets into actionable insights, empowering farmers, agronomists, and researchers to optimize productivity and sustainability. As we approach 2025, I expect AI to play an increasingly critical role in data analysis and decision-making. Outcome: AI integration in agriculture progressed faster than anticipated, evolving from simple chatbots to sophisticated AI agents powered by Large Language Models (LLMs). These agents now engage in conversations, complete tasks, and show some degree of autonomous behavior, fundamentally reshaping farm data analysis and decision making. Looking Ahead: In 2025, Generative AI is set to become a cornerstone for agricultural companies. Advanced platforms using AI already exist, unlocking real-time insights from vast datasets, empowering agronomists and researchers to optimize product performance and accelerate decision-making. Adoption of these tools will help to validate the efficacy of solutions like biologicals, which play a critical part in advancing sustainability. #3 Data-Driven Product Development for Grower Success Data-centric technologies are optimizing field trial outcomes, enhancing decision-making and driving innovation in agricultural practices. Beyond streamlining operations, these technologies were expected to enable breakthroughs across the agricultural value chain in 2024, accelerating the pace of innovation. Outcome: Over the past 12 months, we witnessed a growing focus on using data to meet growers’ evolving needs, setting the stage for further integration and innovation in 2025. The adoption of digital tools varied significantly across regions and farm sizes. In North America, 61% of farmers used digital agronomy tools, 51% adopted precision agriculture hardware, and 38% used remote sensing technologies. However, uptake was lower in other regions and among smaller farms. Looking Ahead: In 2025, I expect data-driven solutions to become integral to efficiency across the agricultural value chain. From accelerating product trials to optimizing precision crop nutrition protocols, these tools will deliver actionable insights, enhancing productivity and supporting regenerative practices in real-time. As biodiversity becomes a priority, data tools will measure the impact of farming practices on local ecosystems, helping companies achieve sustainability and productivity goals. #4 Meeting Resource Constraints with AI and Machine Learning To meet the needs of a growing global population, amid finite resources and rising costs, agriculture must embrace innovations like biosolutions, AI, and machine learning. These technologies improve productivity while reducing environmental impact. In 2024, we expected an increased focus on innovations to address resource constraints, particularly with the use of biosolutions. Outcome: Biosolutions development and adoption progressed, with Brazilian farmers leading the way (64% adoption rate) due to government support and high fertilizer costs. While this did happen in 2024, adoption rates varied significantly across regions. Looking Ahead: In 2025, as more companies adopt “nature positive” targets, real-world pilots and trials will increase to validate new biosolutions and other innovative products. These trials will be crucial in assessing the impacts of specific crop varieties, products, or practices on yield and the environment, ensuring solutions effectively tackle climate change, resource constraints, and growing global food demands. #5 Digital Twins: The Untapped Frontier in Agriculture Digital twins are virtual replicas of real-world systems, enabling simulation and prediction without physical testing. Although widespread in healthcare and manufacturing, they are underused in agriculture, despite its potential to improve precision and cut costs in field trials. We predicted that 2024 would see an increase in the use of digital twins. Outcome: In 2024, agriculture lagged behind other industries in adopting digital twins and synthetic data, hindered by challenges like limited data integration, slower technology adoption rates, and the complexity of replicating dynamic environmental variables in virtual models. However, interest in digital twins has grown, setting the stage for future advancements. Looking Ahead: In 2025, digital twins will gain traction in agriculture, being the ideal time for its adoption and scaling. By enabling virtual testing of variables like soil types and weather conditions, they will reduce time and costs in product testing and support more precise innovation in agricultural practices. Integrating synthetic data will further improve field trial efficiency, accuracy, and safety. This combination allows researchers and agronomists to model scenarios that were previously impractical, supporting smarter resource use and adaptive management strategies. These models will help us to better understand and manage the ecological impact of various practices, paving the way for a more resilient and sustainable agricultural future. Building a Sustainable Future for Agriculture Reflecting on 2024 and looking forward to 2025, it’s clear that the agricultural sector is rapidly evolving. And while many of my 2024 predictions were on point, the pace and direction of change was surprisingly slow. So, in 2025, I anticipate even more advancements in data-driven agriculture, as well machine learning and other innovative solutions to allow us to tackle resource constraints. And I expect this to be in combination with the adoption of disruptive technologies like digital twins. It’s also clear to me that the impact of artificial intelligence in agriculture, will continue to be a key driver in shaping a regenerative agricultural future. In conclusion, to fully leverage these advancements in 2025, agricultural stakeholders should: Prioritize data integration and interoperability. Focus on developing user-friendly solutions. Emphasize education and support for new technologies. Foster collaboration between technology providers, research institutions, and agricultural stakeholders. Adapt solutions to regional needs and farm sizes. By embracing these trends and focusing on practical implementation, agriculture can build a more resilient, productive, and sustainable future for food production. The commitment to biodiversity and nature-positive practices, alongside technology-driven advancements, will be key in shaping a truly sustainable agricultural future.
[前沿资讯 ] CLAAS Farm Machinery Now Connects With CropX’s Precision Agronomy Platform 进入全文
CropX；Global Ag Tech Initiative；
CropX, the leading global platform for precision agronomy, has announced a new powerful digital connection with CLAAS, a global manufacturer of agricultural machinery. Through this new integration between CropX’s agronomy platform and CLAAS’ digital farm and fleet management platform, users of CLAAS equipment can bring their machine data into CropX for visualization, analysis, and creation of variable rate application tasks. A new integration between CropX’s agronomy platform and CLAAS’ digital farm and fleet management platform, users of CLAAS equipment can bring their machine data into CropX for visualization, analysis, and creation of variable rate application tasks. The CropX agronomic farm management system combines data gathered from satellites and the field with agronomic knowledge and advanced AI-assisted machine learning to offer a comprehensive understanding of what is happening in the soil. Users get a snapshot of field conditions and receive recommendations on irrigation timing and quantity, nitrogen leaching, and fungal disease crop protection. In addition, users can import data from the world’s most popular agricultural machinery brands, of which CLAAS is the latest. The data can be visualized and used to create maps for variable rate application of seeding, fertigation, and irrigation, and harvest yield maps can be added for planning future seasons. CLAAS is a major agricultural machinery manufacturer based in Germany. The company produces combines, self-propelled forage harvesters, tractors, loaders, agricultural balers and grassland harvesting machines as well as cutting-edge agricultural information technology. Taha Ghaznavi, Global SVP of Product at CropX, said, “With this integration, CropX continues to expand the universe of farming machinery that can connect to the CropX system. Data integration is the key to unlocking value from data sets, enabling advances in precision agriculture.” “With CLAAS Connect and the new interface with CropX, we offer our joint customers enhanced interoperability between systems,” said Wolf-Christian von Wendorff, SVP of Global Digital Solutions at CLAAS. “The seamless integration of agronomic data into the CropX platform enables customers to boost their productivity through informed decision-making, thereby enhancing the efficiency of practices such as irrigation.”
[前沿资讯 ] AI doesn’t need to stay a black box 进入全文
Wageningen University & Research；
Artificial Intelligence may yield impressive results, but its inner workings often remain a mystery. Is our only option to blindly trust these algorithms? Fortunately, it isn’t. Innovations in the field of Explainable AI can offer us more insight into AI, or even allow AI to explain itself. Artificial Intelligence (AI) has access to immense computing power, making it highly suitable for complex analyses. Vast data sets or extremely precise measurements can be processed faster and with greater accuracy through the application of AI technology. Medical diagnoses or food safety checks are two good examples of this. At the same time, the new technology is also giving rise to doom scenarios among laymen, and some scientists are likewise sceptical about the innovations in this field. ‘An important reason for these misgivings is the black box: the calculations and analytical steps a neural network carries out often remain unknown. Scientist especially want to properly understand their digital tools. What if an AI is prejudiced towards certain conclusions, but we don’t initially notice this?’ We interviewed Bas van der Velden, team lead Data Science at Wageningen Food Safety Research (WFSR). He and his colleagues are investigating ways to create more insight into the inner workings of AI. Explainable AI ‘What is colloquially known as AI are usually self-learning algorithms, or so-called “deep learning” models. They are trained using relevant data and can program themselves,’ Van der Velden explains. ‘AI systems create many so-called ‘non-linear’ correlations. These are complex mathematical equations that can be used to plot the movements of a pendulum or the multiplication of cells. Deep learning models can feature millions of these equations.’ The high complexity of deep learning models makes it virtually impossible to manually check the conclusions of an AI system. ‘Even if someone were to check all those equations, this wouldn’t necessarily lead to deeper insight into the inner workings of the network,’ Van der Velden claims. This complex network of correlations also allows AI systems to perform highly in-depth analyses. Be we don’t quite know how. ‘Under the heading ‘Explainable AI’, data scientists are proposing various solutions to improve the transparency of such neural networks.’ To experiment with one of these solutions, Van der Velden and his colleagues set up a project, also known as a Small Innovative Project. ‘These are projects in which we try to develop a proof of concept for research of a potentially larger scope.’ The objective: explainable AI. Checking for growth hormones With the AI they aimed to build for this project, the researchers wanted to contribute to the process of checking cattle for growth hormones. These substances, which make cattle unnaturally bulky, are prohibited. Currently, there are various chemical-analytical methods to test animals for growth hormones. Could the AI contribute to these checks? Van der Velden and his colleagues trained an algorithm to test cow urine for traces of banned substances. In order to ensure accurate results, the scientists fed the system with data from a mass spectrometer. This is a device that can accurately measure the molecular mass, allowing it to determine the structure of chemical connections in a urine sample, for example. Artificial intelligence is able to perform myriad exceptional tasks, especially when it comes to analysing large quantities of data. At the same time, many AI models a black box; they output answers without substantiation. But it doesn’t have to be this way. Researchers Bas van der Velden and Zuzanne Fendor are working on Explainable AI: artificial intelligence that explains itself. These models offer insight into the way they function. This not only makes them more reliable, but also more informative. The neural network learned to analyse mass spectrometer data and separate the samples containing traces of growth hormones from the ones that did not. The results were impressive: the AI had an accuracy rate of 90%, which is about as good as the current statistical methods. ‘We expect that AI will work even better at a larger scale,’ Van der Velden says. ‘The algorithm may allow us to find unknown growth promotors as well, and could also be used for other applications, such as food safety checks. No AI for straightforward issues These are lovely results, but thus far remain unsubstantiated. What properties did the AI draw from to formulate its conclusions? ‘To find out, we used a common framework from game theory, named SHAP. This framework establishes connections between the data entering the model and the results it produces,’ Van der Velden explains. The framework pointed out a specific chemical structure as the key property of detecting growth hormones in urine samples. ‘Domain experts confirmed that this analysis is correct. SHAP managed to show what the AI based its analysis on.’ ‘Explanations like these not only allow us to understand how a neural network works, but could also help pinpoint the origins of potential mistakes made by the AI. These insights can be used to improve algorithms, so they don’t continue to make the same mistakes.’ This is important, since AI will be used for large-scale, complicated tasks. ‘Straightforward issues likely don’t require Explainable AI, but if a neural network is going to make medical diagnoses or estimate the risk of relapses, transparency becomes increasingly important.’ AI that explains itself While Van der Velden is proud of the results of the project, he does not consider game theory solutions to be the future of explainable AI. ‘There are roughly two main varieties of Explainable AI: subsequent explanation, which is what we used in this project, and an explanation functionality built into the AI itself. AI that teaches itself to explain, so to speak.’ Methods that provide subsequent explanation certainly have their merits, but they come with some drawbacks, too. ‘Of course, it is great that these systems can be applied to any AI, but the results aren’t of the same high quality.’ ‘The integrated explanation is more elegant,’ Van der Velden explains, using a word with a very specific meaning to mathematicians. ‘With elegance, I mostly mean that the explanation is a lot more specific, tailored to the purpose of the AI. Since the underlying functionalities are developed along with the AI, they can be built to suit the user’s needs. I believe this is a major benefit. After all, biologists need different information than food processing companies.’ Self-explaining AI already offers more insight into its own inner workings even when the algorithm is still being trained. ‘What you get is a feedback loop, allowing you to further optimise the neural network.’ Social responsibility ‘Many scientists who work with artificial intelligence believe that this technology will significantly change our future. By this, they don’t mean smart kitchen appliances, but far-reaching, significant innovations in the way we process data,’ Van der Velden explains. ‘This is the time to determine the direction of that transformation. As an AI researcher, I consider it my social responsibility to take the utmost care when it comes to the risks of artificial intelligence.’ Van der Velden doesn’t suggest we’ll encounter science-fiction doom scenarios. ‘No, what I’m talking about is the responsible development of these powerful algorithms. Opening the black box of AI will allow us to more consciously deal with artificial intelligence.’ Bas van der Velden Team leader Data Science Research Project Small Innovative Projects Team Wageningen Food Safety Research
[前沿资讯 ] AgriRobot Secures Funding to Accelerate Development of Autonomous Agricultural Safety Software 进入全文
AgriRobot；Global Ag Tech Initiative；
AgriRobot, a pioneering agritech company developing safety software for autonomous agricultural robots, today announced the successful first closing of its latest funding round. This investment brings the total capital raised by the Danish innovator to more than €2M and will fuel AgriRobot’s mission to revolutionize agriculture by enabling safe and efficient autonomous agricultural operations. AgriRobot is developing certifiable software that eliminates the need for human operators to monitor robots in the field. Leveraging advanced technology from self-driving cars and last-mile delivery robots, AgriRobot’s software ensures the safety of autonomous tractors and robots, paving the way for a new era of efficient and sustainable farming. The round was led by Norminal Ventures with participation from Tall Grass Ventures. The funds will be used to accelerate the development of their safety software while they continue to raise additional capital. “We are excited to announce this significant milestone for AgriRobot,” said Tommy Ertbølle Madsen, CEO at AgriRobot. “This first close of our investment round will enable us to accelerate the development and deployment of our innovative software, helping farmers worldwide achieve safe and sustainable agricultural practices.” “By addressing critical safety challenges, AgriRobot’s software empowers farmers to increase efficiency, reduce labor costs, and enhance crop quality while minimizing risks,” said Chris Edwards, Managing Partner at Tall Grass Ventures. “With a highly technical and knowledgeable team, we are excited to watch as they bring functional safety to autonomous agricultural operations.” AgriRobot’s software is designed to be modular and easily scalable, allowing for integration with various autonomous agricultural systems. With a strong focus on safety compliance, AgriRobot is committed to providing equipment manufacturers with reliable and trustworthy solutions. Founded in 2021 by a team with deep expertise in advanced robotics, agricultural compliance and functional safety analysis, AgriRobot is developing leading safety solutions for OEMs to enable the transition from semi to fully autonomous operations.
[前沿资讯 ] Ninjacart Startup Program Launches to Empower FoodTech and AgTech Startups in Accelerating Growth 进入全文
Ninjacart；Global Ag Tech Initiative；
Ninjacart, India’s leading agri-tech startup transforming the agricultural ecosystem through technology and data, announced the launch of its Ninjacart Startup Program.This agri-tech-focused startup program aims to accelerate the growth of early-stage FoodTech and AgTech startups by providing access to cutting-edge technology, venture capital partners, financial backing, and expert business advisory. With nearly a decade of experience in transforming agricultural ecosystems through technology and data, Ninjacart has partnered with agri-tech innovators globally to optimize supply chains and solve complex distribution challenges. The Ninjacart Startup Program leverages this expertise, network, and proven solutions to help startups scale faster, drive meaningful change, and lead the future of food distribution. The Ninjacart Startup Program offers four key benefits to participants: 1. Access to Ninjacart’s Advanced Technology: Renowned for its comprehensive and innovative technology across the agri-tech ecosystem, Ninjacart’s proprietary solutions set an industry benchmark. Through the Startup Program, startups gain access to a focused suite of Ninjacart’s supply chain management tools to drive growth or enhance operational efficiency. Growth tools include demand forecasting, sales management, pricing intelligence, campaign management, and customer app modules. For operational efficiency, modules for procurement, inventory management, workforce management, logistics, and catalog management are available. 2. Pitch to VC Partners: Startups will have the chance to present to top VC partners such as Syngenta Group Ventures and Base Capital on Demo Day, scheduled for February 2025. 3. Financial Backing: Ninjacart offers credits up to $50,000 to offset platform and implementation fees during the first six months of participation. 4. Expert Business Advisory: Participants will gain access to Ninjacart’s domain expertise and leadership to build scalable supply chains for fresh produce, meat, and staples, offering tailored guidance to shorten their path to profitability. The program is open to emerging startups that were founded in 2020 or later, have raised up to $1 million in funding, and operate outside India. Eligible startups must be post-revenue and focused on innovating the food supply chain. “At Ninjacart, we’ve always believed in the transformative power of technology to solve critical supply chain inefficiencies,” said Kartheeswaran KK, Co-Founder and CEO, Ninjacart. “The Ninjacart Startup Program is designed to empower innovators who are driving systemic change in food systems. By offering our expertise, technology, and network, we aim to help startups accelerate their startup journey and propel the collective transformation of the global agriculture ecosystem.” Applications for the inaugural cohort of the Ninjacart Startup Program are now open. Startups eager to scale and transform food systems can visit here or contact nsp@ninjacart.com to apply.
