AI Applications In Agriculture

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

While a $2 million tractor plants wheat across 7,500 acres, the modern farmer of today is on a Zoom call. The steering wheel hasn't been touched in hours. ➡️ Welcome to agriculture's extinction event for human labor. Nelson's Washington state farm runs itself; tractors navigate by AI, sensors decide when to spray, cameras identify individual weeds among 750 million plants. McKinsey's data confirms the revolution: 15% of large farms already deploy robots, but that's about to explode. John Deere's "See & Spray" tech scans 2,100 square feet per second, slashing herbicide use by two-thirds. ➡️ The economics are brutal: Tortuga's strawberry-picking robots work 24/7 without breaks. Israel's Tevel deploys flying robots that harvest fruit autonomously. Yaniv Maor, Tevel's CEO, doesn't mince words: "Growers who don't adopt robotics won't survive, they simply have no choice." Taylor Farms just acquired FarmWise (acquired)'s AI weeders to cut labor costs permanently. ➡️ Every component of human farming faces replacement. SoilOptix Inc maps entire fields' microbial health without human sampling. Virtual fences zap cattle who stray from GPS boundaries. Monarch Tractor's electric tractors run 14 hours unmanned. Microsoft's Ranveer Chandra envisions farms where "every drone flight updates the farm's unique AI model," learning, adapting, eliminating human judgment. ➡️ The barriers crumbling: Connectivity gaps filled by edge computing. Costs plummeting as venture capital floods in. Oishii's vertical farms already run robotic harvesters that handle berries more gently than human hands. The "small army of weeders and pickers" becomes two supervisors watching screens. 👉 2/3 of American farms already use digital management systems 👉 Robots reduce herbicide use by 66%, work 24/7 👉 750 million plants per 5,000-acre farm monitored individually ❓ When machines know your soil better than you know your children, are you still a farmer or just a spectator to your own obsolescence? Read a summary of the article, created with Futurwise, here: https://lnkd.in/gtsmrbdu #AgTech #Automation #FutureOfFarming #AI #Robotics #Leadership ---- 💡 𝗪𝗲’𝗿𝗲 𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗮 𝘄𝗼𝗿𝗹𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝘀 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰, 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗿𝘂𝗹𝗲𝘀 𝗮𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝗿𝗲𝘄𝗿𝗶𝘁𝘁𝗲𝗻 𝗶𝗻 𝗳𝗿𝗼𝗻𝘁 𝗼𝗳 𝗼𝘂𝗿 𝗲𝘆𝗲𝘀. I dive deep into these shifts, and I can bring these thought-provoking insights and actionable strategies to your next event. If you enjoyed this content, I help audiences think bigger, adapt faster, and embrace the future with confidence. Let’s connect and talk. 🚀

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.

Seeding the Future: Leveraging Artificial Intelligence (AI) and Molecular Breeding (MB) to produce Super Seeds!! In the evolving landscape of agriculture, the fusion of biotechnology and digital technology is creating a revolution, especially with the emergence of 'Super Seeds' through AI and MB. 🌱 AI-driven Insights: AI analyzes vast datasets to provide real-time insights on soil quality, weather patterns, and crop health, enabling farmers to make data-driven decisions for higher yields and reduced resource usage. 🧬 Molecular Breeding: Harnessing the potential of genomics, we're breeding crops with desirable traits, like disease resistance, drought tolerance, and higher nutritional value. This precision breeding accelerates crop improvement. One company which is at the forefront of this convergence is OlsAro Crop Biotech AB, a Swedish company. OlsAro is blessed with an amazing team including Elén Faxö, Henrik Aronsson, Olof Olsson, and Sofia Ström OlsAro specializes in the cultivation of climate-resilient crop varieties, boasting exceptional salt and heat tolerance. Through the application of cutting-edge MB techniques and their exclusive AI-driven platform it is quietly reshaping the agricultural landscape. Their salt-tolerant crops possess the remarkable ability to flourish in saline lands, once deemed unsuitable for agriculture. Simultaneously, their heat-resistant varieties thrive in the harshest weather conditions. This groundbreaking achievement holds immense potential for significantly bolstering global agricultural production and fortifying food security in diverse regions. Notably, OlsAro's wheat strains have surpassed traditional varieties in extensive field trials across Bangladesh, Pakistan, Oman, and Kenya, where saline land had posed insurmountable challenges. It is estimated that approximately 1.5 billion hectares of land worldwide is classified as saline or sodic, and a significant portion of this land is not under cultivation. Other leaders in this space who are working at the convergence of biotech and digital tech are Inari and Absolute®. The fusion of biotech and digital tech isn't just a game-changer but a necessity to feed our growing global population while conserving resources. #UnclutterFoodAgriculture

Sometimes, a strong master’s thesis — especially when supported by a professional research institute like ILVO — leads to insights that matter far beyond academia. This is one of those cases. In his thesis, Senne Vrins explored why AI-driven weed management, despite its promise to cut pesticide use and improve sustainability, is still struggling to gain traction in Flemish agriculture. The findings are clear: the delay is not because the technology doesn’t work, but because there is a poor understanding of ecosystem orchestration. Farmers, policymakers, AgTech startups, contractors, and researchers all act in their own silos, while success requires alignment across regulation, data-sharing, skills, and business models. Using Ron Adner’s Wide-Lens framework, the study shows that adoption barriers — from cost–risk imbalances to unclear data ownership — are interdependent. Without orchestration, each stakeholder waits for others to move first, creating a stalemate. What is missing is coordinated thinking and governance that actively synchronizes incentives, rules, and trust across the ecosystem. The result is an article that is both academically rigorous and highly relevant for practitioners. It makes the case that AI-driven weed management will only succeed if we shift from isolated technology pushes to ecosystem thinking and orchestration — with initiatives like DjustConnect as key enablers. #AIinAgriculture #AgTech #SustainableFarming #PrecisionAgriculture #ILVO #OpenInnovation #EcosystemOrchestration

📊 Applications of Statistics in Agriculture: Tools, Purpose, and Real-World Examples 🌾 Statistics is transforming modern agriculture — from improving crop yields to enhancing agribusiness decisions. Here's a quick overview of how different statistical tools are driving agricultural innovation: ✅ Crop Yield Prediction Tool: Regression Analysis Purpose: Predict crop yield based on factors like rainfall and fertilizer. Example: Forecasting wheat yield from seasonal rainfall data. ✅ Soil Health Assessment Tool: Descriptive Statistics, Cluster Analysis Purpose: Summarize and group soils based on fertility. Example: Grouping soil samples by pH and organic matter content. ✅ Pest and Disease Management Tool: Probability Distributions, Time Series Analysis Purpose: Model frequency and timing of pest outbreaks. Example: Predicting locust swarms after monsoon rainfall. ✅ Breeding and Variety Trials Tool: ANOVA, Experimental Designs (RCBD, CRD) Purpose: Compare different crop varieties. Example: Testing new rice varieties for higher yield. ✅ Agricultural Marketing Tool: Time Series Forecasting Purpose: Predict commodity price trends. Example: Forecasting onion prices for market planning. ✅ Irrigation and Water Management Tool: Correlation Analysis Purpose: Understand relationships between irrigation and crop performance. Example: Analyzing irrigation frequency and maize yield. ✅ Precision Agriculture Tool: Cluster Analysis Purpose: Classify farms into management zones. Example: Dividing fields by nitrogen requirements for targeted fertilization. ✅ Sustainability and Risk Management Tool: Probability and Risk Models Purpose: Analyze risks like droughts and climate impacts. Example: Calculating drought risk for cotton farmers. ✅ Post-Harvest Loss Analysis Tool: Chi-square Tests Purpose: Identify causes of storage losses. Example: Associating storage methods with grain spoilage rates. ✅ Livestock Productivity Studies Tool: Regression Analysis Purpose: Predict livestock output based on feeding patterns. Example: Forecasting dairy cow milk production from feed intake. 🌱 Key Insight: "Statistics isn't just about numbers — it's about making smarter, data-driven decisions that transform agriculture sustainably and profitably."

#AI | #Blockchain : MahaAgri-AI Policy 2025-2029 .  The key objectives that the department of Agriculture seeks to achieve through this policy are : 1. Develop and deploy a statewide food traceability and quality certification platform as part of #DPI : Establish a digitally integrated platform that ensures end-to-end traceability of agricultural produce and enables verification of food quality through credible government backed and internationally recognised certifications. Leveraging AI, blockchain, QR codes, and #IoT, the platform will enhance transparency, support compliance with national and international standards, and improve market access for farmers and producer collectives. 2.  Promote Farmer Centric Design and Adoption: Ensure farmers are co-creators in AI solution design by enabling participatory model development, multilingual advisory delivery, and community-based piloting mechanisms 3. Deploy Remote Sensing-Based Engine as a Shared Digital Public Good for the state: Deploy a unified, AI-enabled Remote Sensing Intelligence Engine to serve as a shared digital public good across multiple departments. This engine will process satellite imagery, drone feeds, and GIS datasets to generate high-resolution insights on land use, crop health, water availability, soil moisture, vegetation indices, and disaster risk. 4. Build Digital Public Infrastructure for Agriculture (DPI-A): Operationalize the Agriculture Data Exchange (ADeX), expand weather and soil sensor networks, and integrate with platforms such as Agristack and MahaAgriTech to support AI readiness 5. Mainstream GenAI and Emerging technology across #Agriculture value chain: Deploy context-specific GenAI and emerging technology enabled tools for crop planning, disease and pest prediction, irrigation management, supply chain optimization, post harvest handling, and market access.

𝗕𝗲𝘀𝘁 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗔𝗜'𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 𝗶𝗻 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲: 𝗠𝗮𝗵𝗮𝗿𝗮𝘀𝗵𝘁𝗿𝗮 𝗙𝗮𝗿𝗺𝗲𝗿𝘀 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗬𝗶𝗲𝗹𝗱𝘀 𝗯𝘆 𝟮𝟬% People tend to focus only on the parts where technology brings misery, but we need to realise that technology is actually a gift. The Microsoft-AgriPilot.ai partnership in Maharashtra proves this point spectacularly. Their innovative "no-touch" approach using satellite imagery and AI analysis has achieved a 20% increase in crop yields for small-scale farmers. How exactly did AI drive this transformation? Well, their solution combines satellite imagery and drone data to create comprehensive farm assessments without setting foot on the land. Then, advanced AI algorithms analyse this data to generate customised recommendations for: · Precise soil nutrient management based on soil composition analysis. · Optimal irrigation scheduling using predictive moisture modelling. · Weather-based planting decisions from pattern recognition. · Early pest and disease detection through image analysis. 👉🏻 What makes this truly amazing? They delivered these insights in local languages like Marathi. This made advanced agricultural science easily accessible to farmers. And the results speak volumes: • Sugarcane grew THREE TIMES larger than conventional methods. • Successful cultivation of exotic crops like strawberries and dragon fruit. • Income increased by up to 10X for small-scale farmers. What sets this initiative apart is their deliberate focus on farmers with less than two acres of land – those who traditionally get left behind in technological revolutions. This exemplifies what I believe about the future of AI – it creates a golden era for all those people who have a compelling vision, care about solving real-world problems, and have the persistence to make things happen. Are we thinking boldly enough about how AI can transform traditional industries? Or are we just "doing the same things a little faster"?

AI, agriculture and the future… How can AI help smallholder farmers adapt and increase productivity? This week, on Episode #166 of the Unlocking Africa Podcast, I sat down with Dr. William Derban, Head of Programs and Partnerships at the Digital Innovations Group for Opportunity International, an organisation that is using AI-driven solutions to support smallholder farmers across Africa. 🌍 Agriculture is the backbone of Africa’s economy, yet many farmers lack access to timely, reliable information to improve yields and protect their livelihoods. That’s where AI-powered tools like UlangiziAI and FarmerAI come in, providing real-time, localised agricultural advice through WhatsApp and other accessible platforms. But here’s what makes Dr. Derban’s approach unique… Instead of just deploying AI, Opportunity International integrates human-centred design, working closely with farmers, local governments, and agricultural experts to ensure solutions are both practical and scalable. One of my favourite insights from our conversation… Many assume access to finance is the biggest challenge for smallholder farmers. But according to Dr. Derban, the real issue is access to information, knowing what to plant, when to plant, and how to respond to shifting climate patterns. AI is now helping bridge that gap. What we discuss with Dr. Derban: ✅ How AI-powered chatbots are transforming smallholder farming across Malawi, Kenya, and Ghana. ✅ The biggest challenges in last-mile AI adoption, from digital literacy to infrastructure limitations. ✅ Why partnerships with telcos, agritech firms, and financial institutions are essential for scaling AI solutions. ✅ The ethical considerations of deploying AI in vulnerable communities—and how to ensure inclusion. AI in agriculture isn’t just about efficiency; it’s about climate resilience, food security, and financial empowerment for millions of farmers. If you’re interested in AI, agriculture, and financial inclusion, this episode is for you. ⬇️ Listen now by clicking the link in the comments below ⬇️ #AI #Agriculture #FinancialInclusion #EmergingMarkets #Africa #ClimateResilience #Podcast #PodcastHost

🚀 AI in Agriculture: Finally Finding Its Feet in the Fields When we talk about AI, most people think of chatbots, robots, or some futuristic automation. But did you know that AI is quietly transforming Indian farms — and in ways that are genuinely useful to our farmers? Today, AI is helping farmers decide when to sow, how much to water, what market rates to expect, and even how to deal with pest attacks. And what’s heartening is this — it’s not just the private sector driving this; the government is playing an active role too. Some initiatives that stood out: ◾ PM-KISAN AI Chatbot — India’s first AI-powered chatbot helping farmers get quick answers about their entitlements. ◾ Digital Agriculture Mission — Over 48 million farmers already have digital IDs. Surveys are mapping croplands at scale — enabling better policies and targeted support. ◾ Krishi 24/7 — AI monitoring agri-news round the clock so that key insights reach both decision-makers and farmers. ◾ ICAR’s AI for Climate-Resilient Agriculture — A crucial step in preparing farmers for unpredictable weather patterns. And on the private side: ◾ Microsoft's AI advisory via SMS — Helping groundnut farmers in AP and Karnataka know the best time to sow — in their own language. ◾ KissanAI — A multilingual AI assistant designed for Bharat, guiding farmers in real-time. This is where the true value of AI lies — when it reaches the ground and actually helps someone grow better crops or save money. But we still have a long way to go: Small farmers still face barriers — lack of digital literacy, patchy internet, and even trust in tech. But solutions are evolving: ◾ SMS-based advisories (yes, even on basic phones) ◾ Digital service centers in villages ◾ Local demonstrations that show results before asking for trust India’s AI-in-agriculture journey is just getting started — but we’re heading in the right direction. This is an area I care deeply about, because if we get this right, it has the power to transform India’s largely agrarian workforce. If you're working on any of these areas, feel free to connect. Would love to exchange ideas. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar