We're excited to share our latest research on intelligent target spraying in controlled agricultural environments. Our team developed a modular, #AI-powered intelligent spraying platform that combines robotics, computer vision, and precision actuation to deliver accurate, efficient, and sustainable crop protection. Leading researcher Samriddha Das #ControlledEnvironment #AgTech #PrecisionAgriculture #AI #Robotics

🌾 Publication Alert! 🚀 Delighted to share our latest research article titled “A vision-guided robotic system with coordinated 2D motion for intelligent target spraying in controlled agricultural environments” published in the Journal of Agriculture and Food Research. This work presents a modular, AI-powered robotic spraying platform that integrates real-time computer vision, precision actuation, and coordinated motion control — paving the way for smart, efficient, and sustainable crop protection in controlled environments 🌱🤖. Grateful to my co-authors Jino Joy, Evans Kofi Wiafe, and Prof. Xin (Rex) Sun for their valuable collaboration and guidance throughout this work. 🙏 #AI #Robotics #PrecisionAgriculture #ComputerVision #SmartFarming #AgriculturalAutomation #ResearchPublication

🐄 The Future of Everything 101: Cows, Cloud, Code 🌾 Farmers once walked rows of crops, now you find sensors, drones, and autonomous tractors humming away, guided by A.I. models instead of muscle memory: Welcome to precision farming, where data, not just dirt, drives the day. 💡 A $5B Industry Rooted in Intelligence Precision agriculture began in the 1990s with simple GPS maps. Today, it’s a $5 billion industry powered by cameras, chips, and algorithms that can analyze fruit growth to the millimeter or can detect a sick cow before a human ever could through special collars with super tech sensors. 🐄 Dairy by Dashboard Some farms now have very cow wearing a sensor collar that monitors digestion, health, and even mood. The system flags problems within hours, saving the farm nearly $1M in a decade and transforming the farmer’s role from caretaker to data interpreter. 🌱 Automation Everywhere From self-driving tractors to A.I. apple pickers, technology is reshaping how food gets from soil to supermarket. Some 70% of large farms now use precision tech; up from less than 10% twenty years ago. 🧠 When AI Becomes the New Farmhand Farmers aren’t being replaced, they’re being augmented by becoming part biologist, part data scientist, part engineer. Each field becomes a feedback system; each moo-cow 🐮 is a data stream. 🚜 The Future of Farming Is Feedback As sensors get cheaper and A.I. models sharper, every acre becomes more adaptive, efficient, and sustainable. The next agricultural revolution won’t be televised it’ll be analyzed. 🤖 Takeaway: AI isn’t just feeding the world. Rather, it is teaching us to think like farmers again by demonstrating how to be patient, observant, and precise. #AI #Agriculture #AgTech #Innovation #Sustainability #MachineLearning #DataDriven #FutureOfWork #AIinAction #Polyneering

📢 📢 📢  A Context-Aware Navigation Framework for Ground Robots in Horticultural Environments 🧑🔬 Peiqi Jin, Tongxiang Li, Yaoqiang Pan, Kewei Hu, Nuo Xu, Wei Ying, Yangwen Jin, and Hanwen Kang 🏫 South China Agriculture University, TongTai Technology Co., Ltd. 💥 Environmental mapping and robot navigation are the basis for realizing robot automation in modern agricultural production. This study proposes a new autonomous mapping and navigation method for gardening scene robots. First, a new LiDAR slam-based semantic mapping algorithm is proposed to enable the robots to analyze structural information from point cloud images and generate roadmaps from them. Secondly, a general robot navigation framework is proposed to enable the robot to generate the shortest global path according to the road map, and consider the local terrain information to find the optimal local path to achieve safe and efficient trajectory tracking; this method is equipped in apple orchards. The LiDAR was evaluated on a differential drive robotic platform. Experimental results show that this method can effectively process orchard environmental information. Compared with vnf and pointnet++, the semantic information extraction efficiency and time are greatly improved. The map feature extraction time can be reduced to 0.1681 s, and its MIoU is 0.812. The resulting global path planning achieved a 100% success rate, with an average run time of 4ms. At the same time, the local path planning algorithm can effectively generate safe and smooth trajectories to execute the global path, with an average running time of 36 ms. https://lnkd.in/ghwA3vq9

In my earlier post, I shared some photos of our project — a robotic arm designed to detect and harvest ripe tomatoes using object detection and machine learning. Here are some clips of the prototype in action, demonstrating how the system identifies ripe tomatoes and plucks them using the custom-built gripper. Seeing the concept come to life through testing was an exciting experience and our step toward automation in agriculture. Grateful once again to my teammates Anjali A and Anamika T ,our mentors Babit Geo Baby and Sreenesh M V, and the amazing support team — Ahamed Ashif, Muhammed lukman Hakim , Anandu Suresh and Kevin Simon. A special thanks to Inker Robotics for their valuable guidance throughout the project. 🙌 #Robotics #AI #MachineLearning #SmartFarming #Automation #Agritech #Innovation #ObjectDetection #InkerRobotics

• Real world robotics remains one of the toughest challenges for AI. • Argentinian researchers have released a new multi-modal dataset from a weed removing robot. • This dataset helps improve situational awareness and AI understanding in agricultural robotics. These advances highlight the progress and ongoing challenges in applying AI to practical robotics for farming and similar real-world tasks. #AI #Robotics #AgricultureTech Source: https://lnkd.in/eRCgT2pn

The first episode of our new AgTech360 series, Harvesting Innovation, is now live! Aaron Wells of Blue River Technology, a John Deere company, kicks things off by sharing how AI and advanced perception systems are transforming tractors into fully autonomous field machines. Tune in here: https://lnkd.in/dfV5VX56 Follow AgTech360 on your favorite streaming platform to stay connected as we explore #AI, #sensors, #robots in the episodes ahead. #AgTech #HarvestingInnovation #FutureOfFarming #AgTechPodcast #InnovationInAg #AutonomousTractors #AgricultureTechnology #JohnDeereGreen

Some milestones feel bigger than the moment itself. We’re proud to have backed Neuralzome Cybernetics in their $2.4M pre-seed round, a team building autonomous, off-road robots that are already making precision #agriculture and #fieldrobotics practical at scale. Neuralzome’s teachable, no-code AI and simulation-driven training cut time, cost and the specialist skills needed to deploy robots which means robotics can actually reach farms, orchards and other real-world sites that need it most. We’re humbled by the warm coverage the story has received across major outlets and grateful for the encouragement from the ecosystem. Thank you to the media, partners and everyone who amplified this moment. This is just the start. Together with Neuralzome, we’re excited to help translate autonomous #agrirobotics from prototype into everyday #impact for industries and livelihoods. Mohan Sivam Aditya Shriwastava

🍎🤖 Robots in the Orchard – The Next Era of Apple Harvesting? 🌱 A new generation of agricultural robots is emerging — machines capable of picking apples faster than human workers with precision and consistency. These robots use AI, sensors, and advanced vision systems to identify ripe fruit, harvest it gently, and work tirelessly around the clock. #agriculture #farming #automation #robotics #innovation #foodsecurity #agritech https://lnkd.in/guQ3XZbB

🦎 Meet the “robot lizard” – multi-legged #robots designed for rough terrain. Unlike wheels or tall-legged machines, they stay low and stable, moving easily through cluttered environments. Ground Control Robotics equips them with advanced sensors like hyperspectral cameras, soil probes, and VOC detectors—perfect for gathering accurate data without harming crops. ✨ Key uses: Smart farming Search & rescue Pest monitoring A clever mix of mobility + precision sensing + adaptable design makes this approach stand out. #DeepTech #AgriTech #Robotics https://lnkd.in/d7VWeXa5