Implementing AI SaaS Solutions in Crop Management

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

Software is Feeding the World's released a white paper last week with a framework on how to go from a proof-of-concept phase to production deployment by looking at four case studies across Bayer | Crop Science, Digital Green, KissanAI and Traive. Kissan AI’s advanced multilingual AI platform provides personalized, voice-based assistance for agricultural needs, empowers farmers, agribusinesses, governments, and nonprofit organizations in India. Kissan AI realized there is a significant amount of agriculture practice and research data available online with agriculture research universities, extension agencies, and government organizations in India. This information is fragmented, difficult to get to and understand for a common farmer. Kissan AI set out to explore the use of GenAI tools to bring all this unorganized data from multiple sources together and build a GenAI model which can answer some of the day to day and basic questions for farmers, and their advisors in a timely and accurate fashion. Kissan AI’s agriculture LLM (called Dhenu) is a fine-tuned open source model based on Llama 3. It uses more than 1.5 million agricultural instruction data sets, produced by using factual data and synthetic data. Dhenu models are used by large agribusinesses for use cases like farmer advisory, conversation commerce and sales copilot. Dhenu models are also available for developers to build Agriculture GenAI applications. Pratik Desai, PhD To learn more about how Kissan AI moved from POC land to actual deployment do get the white paper. You can get it for free at https://lnkd.in/gbcJYfWS

𝐈𝐧𝐝𝐢𝐚, 𝐭𝐡𝐞 𝐠𝐥𝐨𝐛𝐚𝐥 𝐥𝐞𝐚𝐝𝐞𝐫 𝐢𝐧 𝐫𝐞𝐝 𝐜𝐡𝐢𝐥𝐥𝐢 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧, 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐬 𝐨𝐯𝐞𝐫 𝟒𝟎% 𝐨𝐟 𝐠𝐥𝐨𝐛𝐚𝐥 𝐞𝐱𝐩𝐨𝐫𝐭𝐬. However, traditional farming practices have often limited this potential. High input costs, pest infestations, and chemical residue issues in exports have historically posed significant challenges for farmers. The integration of Artificial Intelligence (AI) into agriculture is now transforming this scenario, creating success stories across the nation and revolutionizing farming practices. 𝐆𝐮𝐧𝐭𝐮𝐫, 𝐀𝐧𝐝𝐡𝐫𝐚 𝐏𝐫𝐚𝐝𝐞𝐬𝐡, famously known as the Chilli Capital of India, has emerged as a shining example of AI-powered precision farming. By leveraging satellite-based soil monitoring and automated irrigation systems, farmers in this region are achieving remarkable results. Production has surged by 25%, meeting both domestic and export demands. Simultaneously, pesticide usage has reduced by 40%, ensuring the produce is residue-free and compliant with international standards. This shift has opened up lucrative export opportunities, particularly in premium markets across Europe and the Middle East, significantly boosting farmers’ incomes. In Punjab, a state renowned for its wheat and paddy cultivation, AI tools are being seamlessly integrated into traditional agricultural practices. Farmers here are utilizing satellite imagery and real-time analytics to revolutionize water and disease management. AI-driven irrigation systems have reduced water consumption by 35%, addressing the critical challenge of groundwater depletion in the region. Additionally, during a recent yellow rust outbreak, AI-enabled early detection systems helped prevent a 10% yield loss, saving farmers from significant economic losses. Similarly, Karnataka's Belgaum district is embracing AI for effective crop disease management. Farmers are using computer vision technology to detect leaf blight in tomato and chilli crops with an impressive 96% accuracy. The Indian government is playing a pivotal role in facilitating AI adoption through initiatives under the Digital Agriculture Mission. Farmers can avail themselves of subsidies for drones, sensors, and other AI-based devices through the 𝐏𝐌-𝐊𝐈𝐒𝐀𝐍 𝐬𝐜𝐡𝐞𝐦𝐞. Furthermore, the Indian Council of Agricultural Research (ICAR) conducts 𝐰𝐨𝐫𝐤𝐬𝐡𝐨𝐩𝐬 𝐭𝐨 𝐭𝐫𝐚𝐢𝐧 𝐟𝐚𝐫𝐦𝐞𝐫𝐬 in the practical use of AI tools, ensuring that even small-scale farmers benefit from these technological advancements. AI is effectively addressing some of the most pressing challenges in traditional farming. With the pesticide application, it minimizes chemical residues, making Indian produce export-ready. Weather analytics powered by AI predict rainfall and temperature changes, allowing farmers to adapt and mitigate risks proactively. AI adoption has led to a 20–30% reduction in overall input costs, improving farmers' profitability and financial resilience.

Multispectral + AI for Disease Detection in Crops...! Fungal infection. Nutrient deficiency. Pest attack. To the human eye, they often look the same. But when you add multispectral #drone imaging + #AI, the story changes. Every stress type leaves a unique spectral trace — in visible, red-edge, or near-infrared bands. By analyzing these reflectance patterns, AI can differentiate disease stress from nutrient stress or pest damage — often days before visible symptoms appear. Here’s where it gets even more powerful: ---The #AI doesn’t just diagnose — it maps exact #geospatial #coordinates of diseased patches. ---These coordinates can then be fed directly to spraying drones. Instead of #blanket #spraying, drones can micro-target infected zones, reducing chemical use while maximizing crop recovery. Imagine this workflow in real time: ---A spraying drone equipped with a multispectral camera continuously scans crops. ---The onboard AI detects stress signatures instantly. ---Based on GPS + pixel mapping, the drone calculates precise spraying paths. It applies treatment only where needed, while logging data for farmer dashboards. This is #Drone + #AI in action — not just seeing problems but acting on them. #Challenge: Low-cost spraying drones today don’t support advanced sensors or real-time AI inference. #Solution? I’ll share in my next post — stay tuned.