Essential Data for AI-Powered Agriculture

🌾 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

𝐖𝐡𝐲 𝐆𝐞𝐧𝐞𝐫𝐚𝐥-𝐏𝐮𝐫𝐩𝐨𝐬𝐞 𝐀𝐈 𝐌𝐨𝐝𝐞𝐥𝐬 𝐀𝐫𝐞𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 𝐟𝐨𝐫 𝐀𝐠𝐫𝐢𝐜𝐮𝐥𝐭𝐮𝐫𝐞 Foundation models have transformed remote sensing and weather forecasting, but a critical gap remains between what these models offer and what agriculture actually needs. With climate change and population growth threatening food security, we need AI systems that understand the complex biological processes driving crop growth—not just satellite patterns. This requires integrating weather, soil properties, farm management practices, and socioeconomic factors at field scale. Vishal Nedungadi et al. evaluated existing foundation models on three agricultural tasks across Africa and Europe. Their findings reveal both promise and significant limitations: while models achieved strong performance in crop type mapping in Kenya, they significantly underperformed in yield estimation when input modalities didn't align with task requirements. 𝘒𝘦𝘺 𝘤𝘰𝘯𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯𝘴: - Introduced the CropFM framework defining requirements for agricultural foundation models: daily temporal resolution, ≤10m spatial resolution, and multimodal inputs (satellite, weather, soil, management data) - Systematically compared 2 existing foundation models, revealing fundamental mismatches—most weather models operate at coarse resolution, while satellite models lack key agricultural modalities - Demonstrated empirically that performance degrades when pretraining inputs don't match task needs—a lesson for future model development The research makes a clear case: agriculture needs purpose-built foundation models with farm-scale resolution, daily temporal granularity matching crop growth cycles, and the environmental drivers that mechanistic crop models have long relied upon. https://lnkd.in/eu6FGegG #AIinAgriculture #MachineLearning #PrecisionAgriculture #RemoteSensing #FoundationModels #SustainableAgriculture — Subscribe to 𝘊𝘰𝘮𝘱𝘶𝘵𝘦𝘳 𝘝𝘪𝘴𝘪𝘰𝘯 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴 — weekly briefings on making vision AI work in the real world → https://lnkd.in/guekaSPf

AI can go beyond. It´s decoding ecosystems to empower farmers and optimize agriculture! Last year, US Farmers spent more than USD $48 billion in inputs (fertilizer, crop protection and biological applications). While the adoption of biologicals accelerates and the use of fertilizers does not decrease at the same speed, there’s a pressing need for advanced prescribing tools to guide farmers in optimizing their operations. Imagine using AI to predict changes in the soil ecosystem and generate precise recommendations for the fastest-growing segment of agriculture—biological inputs and fertilizers. #BeCrop is the first digital system to predict soil functionality, powered by Biome Makers Inc., with proprietary AI models integrating a wide range of environmental variables—like soil biology, functionality, physical-chemical, or climate factors—to provide data-driven insights and maps tailored to each field. Farmers receive precise recommendations on input needs for nutrients, biostimulants, and crop protection, boosting yields and promoting resilient soil health. https://lnkd.in/daEbXPwf Let's harness the power of AI to nourish our planet and feed the world. #agriculture #agritech #AI #precisionagriculture #sustainability #farming #soilhealth #biologicalinputs #fertilizers #BeCrop