AI Applications In Agriculture

AI is redefining what’s possible in modern agriculture. Have you tried maximizing parsley cultivation? Consider this: 🌱 52 parsley bunches from a single aeroponic tower occupying just 1 m² 🌱 9-week crop cycle (3 weeks from seed to seedling + 6 weeks from transplant to harvest) 🌱 6 seeds per growing port to maximize uniformity and yield Now imagine combining that system with AI. The Power of AI + Aeroponics Agriculture generates enormous amounts of data every day: Temperature Humidity CO₂ concentration Light intensity (PPFD/DLI) Nutrient EC pH Water usage Growth rates Harvest weights AI can analyze millions of data points continuously and make adjustments faster than any human operator. What AI Can Deliver ✅ 15-30% yield improvement Through optimized environmental control and predictive growth models. ✅ Up to 95% less water Aeroponic systems already use dramatically less water than traditional farming. AI helps optimize every misting cycle and nutrient delivery event. ✅ 20-40% reduction in fertilizer waste By dynamically adjusting nutrient concentrations based on plant growth stages. ✅ Early disease detection Computer vision systems can identify plant stress and nutrient deficiencies days before they become visible to the human eye. ✅ More accurate harvest forecasting AI can predict harvest windows with high precision, helping farms reduce waste and meet customer demand. The Economics Scale Fast A facility with: 1,000 towers 52 bunches per tower 52,000 bunches per harvest cycle Even a modest 10% increase in yield means: ➡️ 5,200 additional bunches every cycle ➡️ More revenue without expanding floor space ➡️ Better return on infrastructure investments The Future: Autonomous Farms The next generation of vertical farms will operate as living AI systems: Cameras monitoring every plant Sensors feeding real-time environmental data AI models predicting growth trajectories Automated nutrient and irrigation control Digital twins simulating thousands of optimization scenarios before changes are made This isn't science fiction. The global AI in agriculture market is projected to grow from billions today to tens of billions of dollars over the next decade as growers seek higher yields, lower resource consumption, and greater food security. The future of farming won't be defined by how much land you have. It will be defined by how intelligently you use every square meter. 🌱 52 bunches. 1 square meter. Millions of data points. One intelligent growing system. #AI #Aeroponics via @agrotonomy #VerticalFarming #SmartAgriculture #FoodTech #AgTech #ControlledEnvironmentAgriculture #MachineLearning #Sustainability #Innovation #DigitalTwin #FutureOfFood #PrecisionAgriculture

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.

The AI revolution in agriculture has little to do with ChatGPT—but there’s an important connection. The real disruption is happening quietly, through predictive mathematical models that are transforming how we breed, grow, protect, and deliver food. These models are already in action—genomic selection is predicting traits from DNA to accelerate plant breeding, Model Predictive Control (MPC) is optimizing greenhouse conditions and harvest timing, and crop and disease simulations are guiding responses to pests and pathogens. These aren’t large language models (LLMs) like ChatGPT. They are structured, multiparametric systems. But here’s the key: the AI wave sparked by LLMs is accelerating their potential. Because of LLM-scale breakthroughs, agricultural models now benefit from: ▪️ Vastly improved compute power for complex simulations ▪️ Scalable storage for genomic, environmental, and phenotypic data ▪️ Advanced tools for handling massive, multidimensional datasets One of the most promising frontiers: digital twins—dynamic virtual replicas of real-world systems that let growers rapidly test interventions in greenhouses before acting. The precedent is powerful: Mercedes-Benz AG and NVIDIA built digital-twin factories in Omniverse, halving coordination time, doubling assembly ramp-up speed, and cutting pilot energy use by 20%. Imagine that level of efficiency applied to food systems such as vertical farms and high-tech greenhouses. Why this matters now: Predictive models in agriculture can ride the same infrastructure wave fueling GPT-scale AI. → Efficiency leaps → Resource savings → Greater resilience across the food supply chain The quiet revolution in food systems is already underway. It’s not about replacing farmers with algorithms—it’s about equipping producers with digital tools that unlock productivity, sustainability, and profit. This is the AI in agriculture we should be celebrating and investing in today—because it’s shaping the resilient food systems of tomorrow. #FutureofFarming #Sustainability #AI #AgTech #DigitalTwin #SustainableAgriculture

Latin America is quietly becoming the world's laboratory for AI-powered agricultural solutions. It is a perfect testing ground with farms ranging of less than an acre to farms which are larger than many small countries. With the region's agriculture market projected to reach $10.4 billion by 2033, we're witnessing innovation at unprecedented scale. The Latin America AI in Agriculture Market is projected to grow from USD 142 million in 2025 to USD 786 million by 2031, reflecting a CAGR of 33.2%. Unlike other regions playing catch-up, Latin America is building smart agriculture from the ground up and development finance institutions are already testing and deploying innovations across the region: In 2023, the Development Bank of Latin America and the Caribbean (CAF) supported by the Multilateral Cooperation Center for Development Finance (MCCDF at AIIB) commenced the creation of a network of high-performance computing centers for artificial intelligence in Chile and Dominican Republic. Applications include improving credit scoring of small farmers through use of alternative information, such as crop cycles and diversification, to facilitate access to credit for farmers for inputs and irrigation equipment.  The World Bank DIME AI Initiative is pioneering the next frontier of impact evaluation, leveraging AI and machine learning to develop and implement research, interventions, and tools that address pressing global challenges. This includes agricultural applications and supports the development of AI-driven solutions for smallholder farmers across developing regions including Latin America. Source: https://lnkd.in/egUHhzXa There are also companies in the region getting ready for global scale. For example, Kilimo in Argentina. This big data irrigation startup has saved more than 4.2 trillion gallons of water across 148,000 acres across the US, Argentina, Chile, Paraguay, Uruguay. Most recently featured in Microsoft's case study on AI for smarter irrigation in Chile, Kilimo combines satellite data and machine learning to deliver field-specific irrigation advice for both large commercial operations and smallholder farmers. Source: https://lnkd.in/e2r_tKsV Latin America is proving that through access to precision agriculture technologies, farmers can make data-driven decisions that optimize input use, reduce waste and increase productivity - exactly what Africa and Asia need to replicate at scale. Latin Americans aren't just adopting AI irrigation - they're creating the playbook that could feed the world more sustainably, backed by major development banks who are willing to invest heavily in smart irrigation infrastructure. What lessons from Latin America's institutional support for AI agriculture could accelerate similar transformations in other regions? #AIforAgriculture #LatinAmerica #SmallholderFarmers #Irrigation #JobCreation #FoodSecurity #AgTech #SustainableDevelopment #CAF #IDB Frédéric Wiltmann Sam Fraiberger Diana Margarita Mejia

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"?

The harsh reality of AI in Agriculture… That first time you hear about AI revolutionizing farming, it sounds like a dream. Automated irrigation, precision planting, real-time crop monitoring — is this kind of magic? 😲 Then reality hits. The truth about AI in agriculture: 1. Farmers don’t want “AI magic”; they want reliable, practical solutions. 👌 2. Data is everything — but collecting clean, usable data in the field? Good luck 3. Connectivity is still a problem. No internet in remote farms = no AI models running in real time. 🌍 4. AI needs training. But historical farm data? Either non-existent, messy, or locked behind paywalls. 5. Edge computing sounds great until hardware breaks in the middle of a field. 😭 5. AI-driven decisions vs. traditional wisdom? Try convincing a farmer to trust an algorithm over years of experience. 6. Costs are still high. Many farms can’t afford high-tech AI solutions, and ROI isn't always clear. 💰 7. Regulations and ethics. Who owns the farm data? Are we creating monopolies on agricultural knowledge? 🤔 8. Nature doesn’t always cooperate. AI models don’t work well when weather, pests, and diseases refuse to follow predictable patterns. But here’s what they don’t tell you about making AI work in agriculture: ➡️ Start with simple automation. AI doesn’t have to be all-or-nothing — small, practical improvements make a difference. ➡️ Farmer collaboration is key. The AI revolution is a mindset shift. Not just buying pricey tech and demonstrating on LinkedIn. ➡️ Data management matters. In fact, it's the new accounting. You won’t survive without it in a few years. ➡️ Adaptability beats perfection. Better a small AI tool now than pen and paper while dreaming of full automation. ➡️ Keep learning. The field (literally and figuratively!) is evolving fast. Stay ahead. Educate yourself and others. Why am I sharing this? Because despite the challenges, AI in agriculture has the potential to change the way we grow food, optimize resources, and feed a growing population. If we get it right, the impact could be revolutionary 🤩 It's my personal and professional belief. 🚜 For those working in AgTech or AI for farming: What’s your biggest challenge right now? Let’s talk solutions & ideas in the comments! #AgTech #AIinAgriculture #PrecisionFarming #MachineLearning #SustainableFarming #PetiolePro

GeoAI vs Computer Vision: Same Models, Different Failures GeoAI and Computer Vision often use the same model architectures CNNs, segmentation networks, and transformers. But in agriculture, they fail in very different ways. Computer Vision in agriculture A computer vision model can:  • Detect crop stress in drone or satellite images  • Segment low-vigour areas with high accuracy  • Produce clean masks and confidence scores Visually, everything looks correct. But the output stays in pixel space. Failure mode:  • No alignment with field boundaries  • No area calculation in hectares or acres  • No linkage to farm, plot, or crop stage  • No direct path to spraying or irrigation decisions The model is right. The result is not usable. GeoAI in agriculture GeoAI works under geographic constraints. The same model must:  • Respect coordinate reference systems (CRS)  • Align outputs with cadastral and field boundaries  • Preserve scale and ground sampling distance  • Convert pixels into georeferenced vector zones Failure mode:  • Small spatial offsets break plot-level actions  • Scale mismatch miscalculates affected area  • Incorrect reprojection creates false stress zones The output may look less “perfect”, but it becomes operational. Real-world impact In production agriculture:  • Spraying, irrigation, and advisories depend on area and location, not pixels  • A 5–10% spatial error can mean over-spraying or missed zones  • Trust breaks quickly when GIS validation fails This is why visually accurate models often die after POC. GeoAI is not Computer Vision on maps. It is Computer Vision constrained by geography, scale, and decision-making. Same models. #GeoAI #GIS #AgriTech #RemoteSensing #ComputerVision #PrecisionAgriculture

🌾 AI in Agriculture — There's a Gap Between the Classroom and the Farm Gate, and It's Getting Wider I was at an ag industry panel recently. Most of the discussion was excellent. But one comment stopped me in my tracks. A panellist representing an research arm of a Uni suggested that AI isn't being used in agriculture, isn't useful for basic tasks, and that students shouldn't worry too much about it. I want to respond to that — because I think it reflects an important and widening blind spot in how we're preparing the next generation of agricultural professionals. The distinction that's being missed: LLMs vs. specialised AI and general misunderstand of AI terms and components. When people say "AI isn't that useful in ag," they're usually thinking about large language models — tools like ChatGPT that are general-purpose and not built for precision agriculture. That's a fair critique of those specific tools. But that's not where the transformation is happening. The real revolution is in machine learning, computer vision, deep learning, and purpose-built AI systems — and the commercial evidence is overwhelming. Just some of the companies already doing this at scale:  SwarmFarm Robotics · AgriWebb · Bitwise Agronomy · GXLab · Ripe Robotics · Ceres Tag · XAG · Halter · Hectre · WayBeyond · Cropsy Technologies · Ecorobotix · Agreena · Bilberry · Sencrop · FruitCast · Fieldwork Robotics · John Deere See & Spray · Carbon Robotics · Semios · CropX · Taranis · Cropin · Aerobotics · Regrow Ag · Orchard Robotics The Australian agritech market is projected at USD $774M in 2025 → $2.38B by 2034 (IMARC Group). These aren't pilots. Commercial deployments, real revenue, real farms, right now. So what do ag graduates actually need to leave university with? Not "learn to code." But three non-negotiables: 1. Prompt effectively — ask the right questions of AI tools to get useful outputs. 2. Critically evaluate what comes back — know when an output is wrong or needs agronomic judgement on top. Domain expertise becomes more valuable here, not less. 3. Integrate technology into real workflows — the confidence to actually deploy these tools on farms, in practices, in supply chains. At Bitwise Agronomy, half our team are agronomists. One of our standard interview questions: give us an example of how you use AI in your day-to-day life. No answer is a red flag — not because we expect developers, but because it tells us how someone approaches new problems. In 2026, curiosity about AI is table stakes. The conversation can't stop at "ChatGPT doesn't know your paddock." The graduates of today will work alongside these systems for the next 40 years. Give them the vocabulary and curiosity to engage with them confidently. Thoughts? Keen to hear from educators, agronomists, and anyone hiring in ag. 👇 #AgTech #PrecisionAgriculture #AIinAgriculture #AustralianAgriculture #AgEducation #MachineLearning #ComputerVision #FutureOfFarming #Agronomy #BitwiseAgronomy

Harnessing Artificial Intelligence for Agricultural Transformation | World Bank Group - https://lnkd.in/gAWsN7iv Published: 2025-11-02 The global agrifood system stands at a critical inflection point. Climate shocks, rising input costs, fragile supply chains, and widening inequality are placing unprecedented pressure on food production and distribution. Small-scale producers (SSPs), who produce one-third of the world’s food, are especially vulnerable. Artificial Intelligence (AI) presents a timely and powerful tool to help reimagine agricultural transformation in ways that are more productive, sustainable, and inclusive. This report presents a comprehensive and development-oriented analysis of how AI can be responsibly deployed across agrifood systems, especially in low- and middle-income countries (LMICs). It moves beyond hype to deliver a grounded roadmap of applications, prerequisites, and investment priorities, while emphasizing ethical, inclusive, and scalable use. A Systems-Level View Anchored in Development Goals: While many AI-agriculture reports focus on technological potential or showcase startups, this report grounds AI in the broader developmental context of food systems transformation, emphasizing public policy goals: climate resilience, food security, inclusion, and sustainability. Additionally, it addresses foundational infrastructure such as energy and data governance alongside AI use cases. LMIC-Centric with Scalable Case Studies: This work prioritizes the unique constraints and opportunities in LMICs, such as energy deficits, the digital divide, and limited human capital, while underscoring the strategic relevance of AI in addressing these challenges. A compendium of more than 60 case studies has been curated to demonstrate the adaptability of AI across diverse sociopolitical contexts, with a strong emphasis on the role of public-private partnerships in driving scalable and sustainable innovation. Emphasis on Digital Public Infrastructure (DPI): The report makes a critical conceptual leap by framing DPI (for example, digital ID, land registries, and data exchange networks) as one of the enabling substrates for scalable AI. This integration of AI and DPI is a unique proposition, positing that AI will not scale inclusively without foundational digital infrastructure that is publicly governed and equitably accessible. Responsible and Inclusive AI Deployment: This report goes beyond technical feasibility 8 to examine ethical and governance dimensions. It identifies risks related to bias, privacy, and environmental sustainability and calls for localized, transparent, and inclusive AI practices. The World Bank Microsoft Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH CGIAR International Rice Research Institute Digital Green EY World Agroforestry Alliance of Bioversity International and CIAT

𝗔 𝗦𝗲𝗹𝗳-𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗩𝗶𝘀𝗶𝗼𝗻 General-purpose vision models trained on natural images struggle with agricultural tasks. Farm imagery differs fundamentally from typical computer vision datasets. Md Jaber Al Nahian et al. introduce Agri-FM+, the first self-supervised foundation model specifically designed for close-field agricultural vision tasks. 𝗪𝗵𝘆 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗮𝗹 𝗩𝗶𝘀𝗶𝗼𝗻 𝗡𝗲𝗲𝗱𝘀 𝗗𝗼𝗺𝗮𝗶𝗻-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗠𝗼𝗱𝗲𝗹𝘀: Agricultural images present unique challenges that differentiate them from natural image datasets: • 𝗦𝗺𝗮𝗹𝗹, 𝗱𝗲𝗻𝘀𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀: Individual plants, fruits, and disease symptoms are often tiny and closely packed together • 𝗦𝘂𝗯𝘁𝗹𝗲 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: Critical for tasks like disease detection, pest identification, and crop phenotyping that require fine-grained analysis • 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝗮𝗹 𝘃𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Significant changes in lighting, growth stages, and field conditions create challenging heterogeneity • 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗹𝗮𝗯𝗲𝗹𝗲𝗱 𝗱𝗮𝘁𝗮: Agricultural annotation is expensive and time-intensive 𝗧𝗵𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: The researchers developed Agri-FM+ using a two-stage continual learning pipeline: • 𝗖𝘂𝗿𝗮𝘁𝗲𝗱 𝗔𝗴𝗿𝗶-𝟭𝟰𝟳𝗞 𝗱𝗮𝘁𝗮𝘀𝗲𝘁: High-quality agricultural images systematically selected from 35 public datasets, filtered for domain relevance and visual fidelity • 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝗹 𝗽𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: First learns general features from ImageNet, then adapts to agricultural-specific data • 𝗦𝗲𝗹𝗳-𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Eliminates dependence on manual annotations while learning structured representations 𝗞𝗲𝘆 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Evaluated across eight diverse agricultural benchmarks covering object detection, semantic segmentation, and instance segmentation: • +1.27% average improvement over supervised ImageNet pretraining under full supervision • +8.25% average gain over random initialization • Strong performance even with limited data: +1.02% over ImageNet with only 10% labeled examples • Consistent improvements across tasks from wheat head detection to plant disease segmentation 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲: This work enables more accurate and efficient computer vision for: • Crop monitoring and yield estimation • Plant disease identification and early detection • Pest management and identification • Automated agricultural robotics and machinery • Precision farming applications The model code and weights will be made publicly available (although they're not up yet). https://lnkd.in/e6HtfmAv #PrecisionAgriculture #AgTech #ComputerVision #FoundationModels #MachineLearning #SelfSupervisedLearning #AgriculturalAI