Using AI To Optimize Crop Yields

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

I really liked this paper because it shows a practical way to make agents learn on the job without touching model weights. Memento stores past task traces as cases, then learns a small neural policy to pull the right ones when planning with tools. The result is strong and cheap adaptation in the wild, with top performance on GAIA validation and solid gains on DeepResearcher and SimpleQA. The most useful detail for builders is that a small, well curated memory works best, not a giant one. For Agriculture and Industry Applications: 1) Continuous learning without retraining: Treat every advisory or workflow as a case with state, action, and outcome. Let the agent improve day by day from farmer interactions, sensor reads, and post‑season outcomes, without fine‑tuning base models. 2) Planner‑executor pattern with tools: Use the planner to decompose tasks and the executor to call tools. In ag these tools include weather APIs, satellite and drone imagery, soil and pest models, market prices, compliance rulebooks, PDFs from ag departments, and ERP data. 3) Case Bank as an agronomy brain: Store both wins and misses. When a new query arrives, retrieve a few similar cases and adapt. Start non‑parametric for speed, add a lightweight Q‑scorer to rank cases once feedback accumulates. 4) Keep K small: Retrieve about four cases per query to avoid noise and token bloat. Curate continuously and prune low‑utility items so retrieval stays sharp. 5) Define rewards that matter: Use field‑level outcomes and ops metrics as feedback signals: yield delta, pest suppression, irrigation savings, task completion time, compliance pass, and advisor override rate. 6) Instrumentation and audit: Log plan steps, tool calls, and retrieved cases so agronomists can review decisions and trust the recommendations. 7) Cost control: Most cost comes from growing input context on harder tasks, not from long answers. Summarize tool outputs, chunk logs, and snapshot intermediate state to cap tokens. 8) Generalize to new regions and crops: Case‑based memory helps on out‑of‑distribution tasks, which maps well to new geographies, varieties, and policy changes.

🚨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

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