AI Applications In Agriculture

The future of our food system sustainability is being developed at the convergence of biology, human innovation, artificial intelligence - and hundreds of millions of bugs! Nature's most efficient protein factories have been hiding in plain sight. While we've been debating sustainable food futures, black soldier flies have been quietly demonstrating how to create abundance from what we've overlooked. I visited the Innovafeed facility in Nesle, France with Mathilde Barge to explore how AI is helping reshape our core food systems. Innovafeed has built something remarkable: a system where these flies - with metabolism 25x more efficient than cattle - transform agricultural by-products into high-quality protein and oils. These ingredients replace resource-intensive fishmeal and fish oil in aquaculture and animal feed, addressing our protein challenge without requiring additional farmland, driving deforestation, or depleting oceans. AI systems continuously analyze millions of data points across their facility, predicting growth patterns and optimizing conditions in real-time. It's running today and producing nutrition with 80% less carbon impact than conventional methods. When we talk about sustainability, we often frame it as a sacrifice. This approach reveals the opposite: abundance through smarter systems. Using technology not to extract more from our world, but to create regenerative loops where outputs become inputs. And it's proof that transformative AI doesn't only emerge from Silicon Valley, but often in unexpected sectors like agriculture where practical problems demand inventive solutions. The technologies pioneered in these unlikely places - where insects meet algorithms - will ultimately reshape how we feed our planet. The future belongs to those who see possibility in what others have overlooked. My gratitude to CEO Clément Ray for the warm welcome at the factory and to Nadège AUDIFFREN and Enzo Ballestra, for making this insightful visit possible! #CircularEconomy #FoodSystems #SustainableInnovation #AI #FutureFarming The Patrick J. McGovern Foundation

Drones, also known as unmanned aerial vehicles (UAVs), are helping farmers perform tasks more efficiently than ever before. From spreading seeds over vast fields to applying pesticides where needed, drones are taking on roles that traditionally required a lot of time and labor. 𝐖𝐡𝐚𝐭 𝐬𝐞𝐭𝐬 𝐭𝐡𝐞𝐬𝐞 𝐝𝐫𝐨𝐧𝐞𝐬 𝐚𝐩𝐚𝐫𝐭 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐬𝐞𝐧𝐬𝐨𝐫𝐬 𝐚𝐧𝐝 𝐢𝐦𝐚𝐠𝐢𝐧𝐠 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬. 𝐓𝐡𝐞𝐲 𝐜𝐚𝐧: - Collect detailed data on soil health and plant conditions. - Monitor crop growth, identifying areas that may need attention. - Optimize irrigation systems by detecting moisture levels. - Conduct land surveys quickly and accurately. By providing this wealth of information, drones enable farmers to make informed decisions, leading to increased productivity and profitability. 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐃𝐫𝐨𝐧𝐞𝐬 𝐟𝐨𝐫 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐓𝐚𝐬𝐤𝐬 Not all drones are the same. There are various types designed for specific agricultural needs. For example: - Multirotor Drones: These have multiple rotating blades (like helicopter rotors) and are excellent for tasks requiring high precision, such as seeding specific areas or spot-treating crops. - Fixed-wing Drones: Resembling small airplanes, they're suitable for covering larger areas and are often used for surveying and mapping. With these technological advancements, it's natural to wonder: Will we soon see farms operating without human workers in the fields? While drones and automation can handle many tasks, the expertise and decision-making skills of farmers remain invaluable. Technology is enhancing agriculture, but it's not replacing the human touch - at least not entirely YET. What are your thoughts on the rise of drone technology in agriculture? Do you believe it will lead to more sustainable and efficient farming practices? #innovation #technology #future #management #startups

There’s nothing like bumping into an Acumen fellow before 6 in the morning and getting an impromptu briefing on the amazing things he’s doing. I loved spending time with Michael Ogundare, Nigerian Foundry member (’21) and co-founder of Crop2Cash, a company that connects smallholder farmers to financial institutions to access credit — and now, skills and advice. Already, the company has 500,000 farmers on its platform. What stunned me most was hearing how Michael is integrating AI into the services provided to farmers. “The farmers are weary of accessing traditional extension services,” he said, “because much of the knowledge hasn’t changed since the ’80s and ’90s. Now, we have 20,000 farmers using our AI service." Essentially, the farmers can call a phone number (they don’t need smartphones) and ask the AI about any problem they’re experiencing or any question they might have. The AI responds in their local language (one of seven) and will call them back when a follow-up is needed — for instance, to fertilize or apply a different input. And here’s the part that took my breath away: the 20,000 farmers spend, on average, 20 minutes daily talking with the AI. They typically call between 7 and 8 p.m., set the phone on a table, put it on speaker and share questions and experiences. They might ask about tomorrow’s weather or share worries or concerns. The results are showing up in the farmers’ productivity. This video shows how Crop2Cash is helping farmers become climate-smart: https://lnkd.in/e5higg2i Of course, these are early days, but the changes to agriculture are suddenly dramatic — and the farmers, at least in this case, are quickly adapting. We have so much to learn. #AgTech #AIforGood #FinancialInclusion #SmallholderFarmers #ImpactInvesting

🌾 Helmets Labeling Crops Published Today in Nature Scientific Data!- Our Method Revolutionizing Agricultural Monitoring globally! Excited to share our latest work published in Springer Nature Scientific Data! Our method is a groundbreaking, cost-effective approach for collecting crop-type data in smallholder farming systems, utilizing GoPro cameras and #AI. The Challenge: Traditional crop mapping relies on expensive field surveys, resulting in critical data gaps where this information is most needed for agriculture monitoring. Our Innovation: 📷 Helmet-mounted GoPro cameras on motorcycles or in the comfort of your car capture roadside images of crops  🤖 A deep learning pipeline to automatically identifies crop types  📍 GPS coordinates create georeferenced crop-type datasets compatible with satellite imagery Key Results:  ✅ 4,925 validated crop-type data points across 17 counties in Kenya  ✅ 92.5% accuracy in crop identification across 8 different crop types  ✅ Dataset dominated by maize (#Kenya's critical food security crop)  ✅ Methodology scales efficiently compared to traditional field surveys Real Impact: This approach directly supports UN Sustainable Development Goal 2 (Zero Hunger) by making agricultural monitoring more accessible in regions where it's absolutely needed. Our collaboration with Kenya's Ministry of Agriculture and local agricultural officers ensures the data serves real-world food security applications. The full dataset is now publicly available on Zenodo. We have millions of raw images from  #Uganda, #Tanzania, #Nigeria, #Senegal, #Germany, #Madagascar, # Bhutan, #Zambia, and beyond to analyze! Proud to work alongside an incredible international team bridging AI, remote sensing, and food security. Special thanks to our partners at RCMRD- Regional Centre for Mapping of Resources for Development, #Kenya Ministry of Agriculture, and all the local agricultural officers who made this possible. Read the full paper: https://lnkd.in/dCcv-aAk #FoodSecurity #MachineLearning #Agriculture #RemoteSensing #Kenya #OpenData #SustainableDevelopment

As #ArtificialIntelligence makes its way into agriculture, we could be witnessing a new #GreenRevolution. Nourishing a global population on track to reach 10 billion by 2050 is a monumental challenge. More than just producing more food, this challenge requires us to prepare for the adverse effects of #ClimateChange, resource scarcity, and shifting global dynamics. Here's how #AI is emerging as a valuable tool in reshaping agriculture: #PrecisionAgriculture: AI-driven systems are enabling hyper-localized farming practices, optimizing everything from water usage to fertilizer application. #ClimateAdaptiveFarming: #MachineLearning is helping farmers with weather patterns, suggesting optimal planting times and crop rotations based on climate data. #VerticalFarming: #AIControlledEnvironments are making it possible to grow food in urban centers, reducing transportation costs and increasing food security in cities. #PredictiveAnalytics: From anticipating pest outbreaks to forecasting market demands, AI is giving farmers the tools to make proactive decisions. AI can integrate these aspects into a cohesive, responsive system. Imagine a future where: Satellite imagery, weather data, and soil sensors feed into AI systems that adjust irrigation and nutrient delivery. Robotic harvesters work alongside humans, guided by AI to pick the ripest produce. AI-driven #SupplyChainManagement ensures that food reaches consumers with minimal waste. However, these developments also lead to many important questions. How do we ensure small-scale farmers benefit from these advancements? What are the implications for biodiversity when AI optimizes for efficiency, and how do we balance increased food production with environmental sustainability? One thing is certain, like the internet, AI is one of those technological leaps that are impossible to ignore. It is now up to us to help shape the direction it takes for the benefit of our civilization. #AIinAgriculture #FutureOfFarming #FoodSecurity #SustainableAgro #ThoughtLeadershipFromEncora

🚨NEW PAPER ALERT!!!!🚨 🚀 Advancing Vidalia Onion Farming with AI & Drones! 🌱🧅 Vidalia sweet onions aren’t just common onions—besides their sweetness they’re a $187M industry in Georgia covering 10,000 acres! Despite its high value, growers still rely on post-harvest grading to determine yield and market classes. That’s labor-intensive, subjective, and therefore, costly. Our latest research has the potential to change the game by using UAVs (drones) and AI-driven imagery analysis to forecast onion yield and market classes up to 30, and 45 days before harvesting respectively. We used NIR & RedEdge texture data to train multiple machine learning (AI) algorithms. ✅Key findings: 🔹 Best yield forecasts at 30 days before harvest (R² = 0.73). 🔹 Medium class onions were predicted most accurately at 45 days before harvest. Why does this matter? 🌍 This is the first step for developing smart-harvesting schedules, market planning, and maximize profitability—all while reducing labor costs and waste. This article is a result of a joint effort of the Precision Horticulture Lab at UGA and the Vidalia Onion Committee. Thanks to you all! 📖 Interested? Read more about our study in the link below. [https://lnkd.in/ehAH_zD4] Authors: Marcelo Rodrigues Barbosa Júnior Lucas Sales Regimar Garcia dos Santos #RonegaBoaSorte #ChrisTyson Luan Pereira de Oliveira #VidaliaOnions #PrecisionAg #AIinAgriculture #UAV #SmartFarming #DronesInAg #YieldPrediction #AgTech

In Washington’s Palouse region, fifth-generation farmer Andrew Nelson is running a 7,500-acre wheat farm while on Zoom calls. His tractor drives itself, guided by AI, sensors, and cameras that decide where to fertilize, spray, or weed. This isn’t an isolated story. Farming is entering a new era: 🚜 Autonomous tractors & sprayers from companies like Deere and Monarch are cutting herbicide use by up to 66%. 🚜 Robotic fruit pickers & drones (Oishii’s Tortuga robot, Tevel’s flying harvesters) are easing labor shortages. 🚜 Data-driven “digital twins” of farms are helping farmers target irrigation and pest control with precision. 🚜 Virtual fencing is changing livestock management with GPS-enabled collars. The goal? Smarter, more sustainable farming—optimizing every drop of water and every seed, while letting farmers focus on strategy, not hours in the cab. As Microsoft’s Ranveer Chandra puts it, “Every time a drone flies or a tractor plants, it’s updating the farm’s own AI model.” The autonomous farm won’t replace farmers—it will amplify them. And it’s happening faster than you think. Read more: https://lnkd.in/eEeW7zef

🚀 Revolutionizing Agriculture: John Deere's AI-Powered Farm Machines 🤖 👉 In the ever-evolving world of agriculture, John Deere, the world's largest agricultural machinery company, is once again at the forefront of innovation, leveraging artificial intelligence to enhance farming practices and reduce environmental impact. Founded in 1837, John Deere has a long history of pioneering new technologies, from the invention of the steel plow to the introduction of GPS-assisted steering systems in the 1980s. Over the past decade, the company has embraced machine learning to develop cutting-edge solutions for modern farming challenges. 👉 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Overuse of Herbicides Traditional methods involve spraying herbicides over entire fields, which is both wasteful and harmful to the environment. 👉 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The See and Spray tractor The tractor is equipped with 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐜𝐚𝐦𝐞𝐫𝐚𝐬 𝐚𝐧𝐝 𝐚 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 that can distinguish between crops and weeds with remarkable accuracy. 🧠 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤? As the tractor moves through the field, its AI-powered cameras capture images of the plants below. The 𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐚𝐧𝐚𝐥𝐲𝐳𝐞𝐬 𝐭𝐡𝐞𝐬𝐞 𝐢𝐦𝐚𝐠𝐞𝐬 and directs automated nozzles to spray herbicides only on the weeds, 𝐫𝐞𝐬𝐮𝐥𝐭𝐢𝐧𝐠 𝐢𝐧 𝐚𝐧 𝟖𝟎% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐡𝐞𝐫𝐛𝐢𝐜𝐢𝐝𝐞 𝐮𝐬𝐚𝐠𝐞 𝐚𝐧𝐝 𝐬𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐜𝐨𝐬𝐭 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 for the farmer. 💡 𝐌𝐨𝐫𝐞 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 The company's combine harvesters, which combine multiple harvesting operations into a single process, use computer vision systems to monitor the size and shape of grains as they are extracted. If the AI detects damaged grains, it alerts the operator to make adjustments, ensuring the highest market value for the crop. Additionally, smart cameras scan the waste being ejected from the rear of the harvester to ensure that no grain is lost, further optimizing the efficiency of the process. Most recently, John Deere has introduced a fully autonomous tractor, the 8R, which utilizes six pairs of stereo cameras to scan the environment for obstacles. Trained AI models help the tractor navigate around these obstacles, allowing it to work independently without real-time instructions. 𝐓𝐡𝐞 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐅𝐚𝐫𝐦? John Deere's ultimate goal is to develop a fully autonomous and precision agricultural system, where machines can determine what to do, execute tasks flawlessly, & even move between fields on their own. While this vision is still a few years away, the company is making steady progress towards this ambitious goal. As John Deere continues to push the boundaries of agricultural technology, the future of farming looks more efficient, sustainable, and environmentally friendly than ever before.👇 ******************************************* • Please 𝐋𝐢𝐤𝐞, 𝐒𝐡𝐚𝐫𝐞, 𝐅𝐨𝐥𝐥𝐨𝐰 • Ring the 🔔 for notifications.

Many of today’s most relevant industries didn't exist a decade ago. Streaming disrupted entertainment. Ride-sharing rewrote transportation. Generative AI is now redefining productivity, creativity, and even software development. What they have in common is this: they didn’t win by competing harder in existing markets. They won by creating entirely new ones—blue oceans, where new value was unlocked by rethinking the fundamentals and introducing new possibilities (see Blue Ocean Strategy book). In agriculture, I believe the next blue ocean is site characterization and analysis and the optimization it enables, powered by #digitaltwins. We’re entering an era where the most impactful gains in yield, efficiency, sustainability, and ROI will not come from more of the same—but from deeply understanding how the land functions: spatially, mechanistically, and holistically. As a soil physicist and remote sensing scientist, I’ve spent years working to quantify and understand how soil and crop systems interact and have worked closely with growers across 6 continents. The truth is: most ag decisions today are made using fragmented, subjective, inaccurate, and overly simplified information. The real breakthrough—the blue ocean—are the new opportunities enabled by combining robust analytical quality soil sensing and remote sensing data. Better sensing provides a much richer spatial and information matrix to understand the relationship between crop genetics, management and the growing environment. Liebig’s Law of the Minimum: yield (like water in the barrel) can only rise to the height of the shortest stave. While not perfect, it provides a powerful quantifying framework and a better way to generate dynamic simulations for optimizing ag production throughout and across fields and growing seasons. Take a pH map. It might suggest that a certain zone needs lime. But if other soil properties (say subsoil aluminum toxicity or drainage) or attributes (the thickness of the sandy loam horizon) are the true yield limiters and can’t be practically corrected, then applying lime won’t improve the outcome appreciably. You’d be raising a non-limiting stave in the barrel and limiting ROI. What if we could measure all the staves independently? A digital twin integrates high-resolution soil and crop data into one spatially explicit system. It shows how all limiting and contributing factors interact in context. ✅ Irrigation gets tuned to plant-available water in the actual root zone ✅ Nutrients and amendments are applied more precisely ✅ Crop yield and quality improve ✅ Scouting becomes targeted and contextualized ✅ Baselines for soil health and carbon become objective and repeatable ✅ Less nutrient loss to the ground and surface water systems To optimize agriculture we need to understand everything better than we do today. Learn more: https://landscan.ai/ #Agtech #SoilHealth #PrecisionAg #YieldOptimization #RegenerativeAg #SustainableAg John Deere Mars Unreasonable

What are Genomic Large Language Models (gLLMs), and how are they transforming plant science? 🌱🧬 ⁉️What is a gLLM? Genomic Large Language Models (gLLMs) are AI models trained to understand complex genomic data, allowing us to make more accurate predictions in plant biology, crop improvement, and environmental adaptation. This enables breakthroughs like designing more resilient crops or improving crop yields in changing climates. gLLMs are changing the landscape of plant science. Here's how: 1️⃣AgroNT - This gLLM is trained on 48 plant species, predicts regulatory elements and estimates promoter strength with remarkable accuracy. ✅Applications: This can help pinpoint genes responsible for drought resistance, enabling the development of crops that can withstand water scarcity. 2️⃣PlantRNA-FM - processes 54B RNA sequences from 1,124 plant species, identifying stress-response elements that help crops adapt to environmental changes. ✅It can discover molecular markers for stress tolerance, allowing breeders to select plants that thrive in extreme temperatures or salinity. 3️⃣ESM-2 may not be plant-specific, but it's predicting 3D structures of plant proteins, accelerating enzyme optimization. ✅This can speed up the development of enzymes that enhance nutrient uptake in plants or improve their resistance to pests. 📌gLLMs like AgroNT prioritize functional SNPs 2.5x faster than traditional methods, speeding up breeding programs. This can reduce the time it takes to create new crop varieties with desired traits like higher yield or improved pest resistance. 📌These models enable knowledge transfer from well-studied crops to orphan species, making agricultural innovation more accessible. By applying insights from high-yield crops to underutilized species, we can boost their productivity and nutritional value. 📍The impact? Faster development of climate-resilient crops, stronger food security, and a deeper molecular understanding of plant biology. For more, https://lnkd.in/gsQCRnkm https://lnkd.in/gn5eN5Zi https://lnkd.in/giAJNMbU #PlantScience #AI #gLLMs #CropImprovement #FutureOfAgriculture