We present the AgriCruiser, an open-source over-the-row agricultural robot developed for low-cost deployment and rapid adaptation across diverse crops and row layouts. The chassis provides an adjustable track width of 1.42 m to 1.57 m, along with a ground clearance of 0.94 m. The AgriCruiser achieves compact pivot turns with radii of 0.71 m to 0.79 m, enabling efficient headland maneuvers. The platform is designed for the integration of the other subsystems, and in this study, a precision spraying system was implemented to assess its effectiveness in weed management. In twelve flax plots, a single robotic spray pass reduced total weed populations (pigweed and Venice mallow) by 24- to 42-fold compared to manual weeding in four flax plots, while also causing less crop damage. Mobility experiments conducted on concrete, asphalt, gravel, grass, and both wet and dry soil confirmed reliable traversal consistent with torque sizing. The complete chassis can be constructed from commodity T-slot extrusion with minimal machining, resulting in a bill of materials costing approximately $5,000 - $6,000, which enables replication and customization. The mentioned results demonstrate that low-cost, reconfigurable over-the-row robots can achieve effective weed management with reduced crop damage and labor requirements, while providing a versatile foundation for phenotyping, sensing, and other agriculture applications. Design files and implementation details are released to accelerate research and adoption of modular agricultural robotics.

Out in the California sun, a new kind of farmhand is hard at work. Powered by solar energy and guided by artificial intelligence, the solar-powered weeding robot for cotton fields is offering farmers a smarter and more sustainable way to tackle weeds. This technology is arriving just in time, as growers across the country face a shortage of available workers and weeds that are becoming increasingly resistant to herbicides. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts and exclusive deals delivered straight to your inbox. Plus, you'll get instant access to my Ultimate Scam Survival Guide -- free when you join my CYBERGUY.COM/NEWSLETTER JOB-KILLING ROBOT LEARNS AT WORK, AND IT'S COMING TO THE FACTORY FLOOR Farmers everywhere are facing a tough reality.

A robot dog equipped with a blowtorch could be used to stop weeds growing on farms, potentially offering a replacement for harmful herbicides. Even highly targeted herbicides can cause environmental problems, affecting local wildlife, and "superweeds" are quickly evolving resistance to the most common weed-killers like glyphosate. In search of an alternative solution, Dezhen Song at Texas A&M University and his colleagues have developed a weed control system that uses a brief burst of heat from a propane-powered torch controlled by a robotic arm, attached to a Spot robot manufactured by Boston Dynamics. Rather than incinerate the weeds, the robot is designed to identify and heat up the centre of the plant, which can stop it growing for several weeks, says Song. "The weeds don't die – you just suppress their growth so it gives your crop a chance to fight the weed." The latest science news delivered to your inbox, every day.

A robot dog equipped with a flamethrower could be used to stop weeds growing on farms, potentially offering a replacement for harmful herbicides. Even highly targeted herbicides can cause environmental problems, affecting local wildlife, and "superweeds" are quickly evolving resistance to the most common weed-killers like glyphosate. In search of an alternative solution, Dezhen Song at Texas A&M University and his colleagues have developed a weed control system that uses a brief burst of heat from a propane-powered flamethrower controlled by a robotic arm, attached to a Spot robot manufactured by Boston Dynamics. Rather than incinerate the weeds, the robot is designed to identify and heat up the centre of the plant, which can stop it growing for several weeks, says Song. "The weeds don't die – you just suppress their growth so it gives your crop a chance to fight the weed." Song and his team first tested the flame nozzle to make sure they could accurately target the weeds' centre.

The smell of burnt vegetation wafted through a lettuce field here one recent summer morning as nearly 200 farmers, academics and engineers gathered to witness the future of automated agriculture. Thirteen hulking machines with names like "Weed Spider" and "Mantis" crawled through rows of romaine. One used artificial intelligence cameras to scan the crops and spray them with herbicides. Yet another deployed robotic arms to cultivate and pick through the foliage. "It's a hurdle for people to get over, but the reality is, the numbers don't lie," said Tim Mahoney, a field representative for Carbon Robotics, a Seattle-based company that created one of the machines on display -- a 9,500-pound apparatus known as the LaserWeeder.

The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. This proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds.

Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project introduces a Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a cutting-edge solution tailored for real-time detection of blackgrass, for precision weed management practices. Leveraging Artificial Intelligence (AI) algorithms, the system processes live image feeds, infers blackgrass density, and covers two stages of maturation. The research investigates the deployment of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By optimising inference speed and model performance, the project advances the integration of AI into agricultural practices, offering potential solutions to challenges such as herbicide resistance and environmental impact. Additionally, two datasets and model weights are made available to the research community, facilitating further advancements in weed detection and precision farming technologies.

Can ChatGPT provide evidence to support its answers? Does the evidence it suggests actually exist and does it really support its answer? We investigate these questions using a collection of domain-specific knowledge-based questions, specifically prompting ChatGPT to provide both an answer and supporting evidence in the form of references to external sources. We also investigate how different prompts impact answers and evidence. We find that ChatGPT provides correct or partially correct answers in about half of the cases (50.6% of the times), but its suggested references only exist 14% of the times. We further provide insights on the generated references that reveal common traits among the references that ChatGPT generates, and show how even if a reference provided by the model does exist, this reference often does not support the claims ChatGPT attributes to it. Our findings are important because (1) they are the first systematic analysis of the references created by ChatGPT in its answers; (2) they suggest that the model may leverage good quality information in producing correct answers, but is unable to attribute real evidence to support its answers. Prompts, raw result files and manual analysis are made publicly available.

In this paper an autonomous system to detect and combat Rumex obtusifolius leveraging autonomous unmanned aerial vehicles (UAV), small autonomous sprayer robots and 5G SA connectivity is presented. Rumex obtusifolius is a plant found on grassland that drains nutrients from surrounding plants and has lower nutritive value than the surrounding grass. High concentrations of it have to be combated in order to use the grass as feed for livestock. One or more UAV are controlled through 5G to survey the current working area and send back high-definition photos of the ground to an edge cloud server. There an AI algorithm using neural networks detects the Rumex obtusifolius and calculates its position using the UAVs position data. When plants are detected an optimal path is calculated and sent via 5G to the sprayer robot to get to them in minimal time. It will then move to the position of the broad-leafed dock and use an on-board camera and the edge cloud to verify the position of the plant and precisely spray crop protection only where the target plant is. The spraying robot and UAV are already operational, the training of the detection algorithm is still ongoing. The described system is being tested with a fixed private 5G SA network and a nomadic 5G SA network as public cellular networks are not performant enough in regards to low latency and upload bandwidth.

When John Deere debuted its first-ever autonomous tractor at CES 2022, it signaled a new era of AI & robotic farming would soon be upon us. While other companies have been talking about autonomous tractors for some time, it's an altogether different matter when the U.S.'s biggest manufacturer of farming equipment signals that this is the future. Still in the trial phase, early versions of the 8R are now being tested by what the company describes as its "paying test cooperators." But since it won't be long before the final production model of the autonomous tractor is rolling off the production line, I thought it would be a good time to sit down with one of the company's computer vision leads, Chris Padwick, the Director of Computer Vision and Machine Learning at Deere's Blue River Technology division, to get an idea of the how the company got to this point. According to Padwick, since its acquisition by John Deere in 2017, Blue River has helped accelerate the farming equipment giant into precision agriculture with its "see and spray" computer vision technology.