AI Technologies For Enhancing Agricultural Productivity

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

🚀 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.

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

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