Solutions for Challenges in Robotic Farm Systems

I spent the last year and a half building autonomous systems for orchards at Bonsai Robotics. The biggest surprise? Connectivity is the infrastructure problem nobody talks about. Everyone focuses on the robotics—the perception systems, the path planning, the manipulation. But when you're operating in a 500-acre almond orchard in Australia or the Central Valley, you're dealing with spotty cellular coverage, dust that degrades signal quality, and distances that make WiFi impractical. The robots can see. They can navigate. They can make decisions. But if they can't reliably communicate with fleet management systems or push telemetry data for analysis, you're running blind. This isn't just an ag problem. I've seen similar challenges in all off-road and remote applications, including marine robotics with Wave Gliders operating thousands of miles offshore, army tanks on the frontlines, and rail vehicles and trucks in rural ODDs. The solution isn't just "add more cellular towers." It requires edge computing architectures that let vehicles operate autonomously when connectivity drops, smart data prioritization that pushes critical telemetry first, and mesh networking between vehicles to create resilient communication networks. Connectivity infrastructure is as important as the autonomy stack itself. You can't deploy at scale without solving both. What connectivity challenges have you seen in deploying hardware in remote environments?

🤖Robotics Sunday🤖 ❗Harvesting broccoli with robots❗ Harvesting broccoli is one of the toughest challenges in farming automation - the crop grows unevenly, requires delicate handling, and matures at different times in the same field. A new generation of robotic harvesters is taking on this task. Equipped with AI-driven vision systems, these robots can detect when each broccoli head is ready, cut it cleanly, and place it gently for packing. The benefits? ✅ Reducing labor shortages in agriculture ✅ Increasing efficiency during peak harvest season ✅ Minimizing crop waste through precise picking Robotics isn’t just transforming factories and warehouses – it’s moving out into the fields to tackle some of the hardest jobs in food production.

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.

🚀 Revolutionizing Agriculture: John Deere's AI-Powered Farm Machines 🤖 👉 In the ever-evolving world of agriculture, John Deere, the world's largest agricultural machinery company, is once again at the forefront of innovation, leveraging artificial intelligence to enhance farming practices and reduce environmental impact. Founded in 1837, John Deere has a long history of pioneering new technologies, from the invention of the steel plow to the introduction of GPS-assisted steering systems in the 1980s. Over the past decade, the company has embraced machine learning to develop cutting-edge solutions for modern farming challenges. 👉 𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Overuse of Herbicides Traditional methods involve spraying herbicides over entire fields, which is both wasteful and harmful to the environment. 👉 𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The See and Spray tractor The tractor is equipped with 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐜𝐚𝐦𝐞𝐫𝐚𝐬 𝐚𝐧𝐝 𝐚 𝐩𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 that can distinguish between crops and weeds with remarkable accuracy. 🧠 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤? As the tractor moves through the field, its AI-powered cameras capture images of the plants below. The 𝐭𝐫𝐚𝐢𝐧𝐞𝐝 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐚𝐧𝐚𝐥𝐲𝐳𝐞𝐬 𝐭𝐡𝐞𝐬𝐞 𝐢𝐦𝐚𝐠𝐞𝐬 and directs automated nozzles to spray herbicides only on the weeds, 𝐫𝐞𝐬𝐮𝐥𝐭𝐢𝐧𝐠 𝐢𝐧 𝐚𝐧 𝟖𝟎% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐡𝐞𝐫𝐛𝐢𝐜𝐢𝐝𝐞 𝐮𝐬𝐚𝐠𝐞 𝐚𝐧𝐝 𝐬𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐜𝐨𝐬𝐭 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 for the farmer. 💡 𝐌𝐨𝐫𝐞 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 The company's combine harvesters, which combine multiple harvesting operations into a single process, use computer vision systems to monitor the size and shape of grains as they are extracted. If the AI detects damaged grains, it alerts the operator to make adjustments, ensuring the highest market value for the crop. Additionally, smart cameras scan the waste being ejected from the rear of the harvester to ensure that no grain is lost, further optimizing the efficiency of the process. Most recently, John Deere has introduced a fully autonomous tractor, the 8R, which utilizes six pairs of stereo cameras to scan the environment for obstacles. Trained AI models help the tractor navigate around these obstacles, allowing it to work independently without real-time instructions. 𝐓𝐡𝐞 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐅𝐚𝐫𝐦? John Deere's ultimate goal is to develop a fully autonomous and precision agricultural system, where machines can determine what to do, execute tasks flawlessly, & even move between fields on their own. While this vision is still a few years away, the company is making steady progress towards this ambitious goal. As John Deere continues to push the boundaries of agricultural technology, the future of farming looks more efficient, sustainable, and environmentally friendly than ever before.👇 ******************************************* • Please 𝐋𝐢𝐤𝐞, 𝐒𝐡𝐚𝐫𝐞, 𝐅𝐨𝐥𝐥𝐨𝐰 • Ring the 🔔 for notifications.