AgriRobots

CropLife Editor Eric Sfiligoj and Amy Asmus, Co-Owner of Asmus Farm Supply are joined by Dan Bjorklund, Technical Agronomy Lead from Landus Cooperative to break down early harvest trends and critical fall agronomy decisions for ag retailers. The post CropLife Retail Week: Fall Anhydrous Truths, Drone Scouting, and Iowa Harvest appeared first on CropLife.

A new artificial strawberry stem mimics natural bending behavior and withstands repeated cutting and healing, offering a low-cost platform for agricultural robotics research.

American agriculture has a long history of adopting technology to improve productivity and efficiency. From advancement in seed genetics, to the precision technology that guides equipment down the field with… Source

arXiv:2610.22765v1 Announce Type: new Abstract: Precise pedicel-targeted grasping in strawberry harvesting remains a critical bottleneck due to the pedicel's small diameter (1.3-3 mm), color similarity with surrounding foliage, and precise force control required for clean abscission zone detachment. As a market-grade crop, strawberry aesthetic value makes bruise-free pedicel detachment essential over direct fruit grasping. Classical approaches struggle to unify vision models, motion planning, and contact manipulation into a robust framework, particularly in occluded canopy environments. Therefore, this work presents a visuomotor imitation learning pipeline for pedicel-targeted strawberry harvesting that unifies perception, motion planning, and contact manipulation from collecting human teleoperation demonstrations, training an Action Chunking with Transformers (ACT) policy with a ResNet-50 visual backbone, and deploying the learned policy autonomously on a Jetson AGX Orin edge

A new reusable strawberry stem made from hot melt adhesives provides a standardized, low-cost testbed for validating robotic harvesting systems.

arXiv:2610.22337v1 Announce Type: new Abstract: Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace. This study introduces the Path-Safe Approach Proximal Policy Optimization (PSA-PPO), a target-conditioned local reaching framework designed for robotic harvesting of nutrient film technique (NFT)-hydroponic lettuce in greenhouses. The proposed PSA-PPO preserves the standard clipped PPO objective while introducing a harvesting-oriented policy formulation that integrates target-conditioned observations, corridor-aware spatial constraints, trajectory-history-based safety encoding, and task-specific success criteria for constrained robotic manipulation. PSAPPO was trained entirely in Isaac Lab and successfully transferred to a physical UR10e robotic arm without any real-world policy fine-tuning. In a controlled physical benchmark against 150 scheduled targets, PSA-PPO achieved the highest reaching rate of

Days after Ukrainian military drones took down several Russian data centers, President Volodymyr Zelenskyy claimed that the campaign had hurt Russia's disinformation efforts. "It suddenly became a little easier to breathe on many social platforms," Zelenskyy said Monday. "There was less hate, less division, and fewer provocations." Zelenskyy isn't the first to assert that the drone operation, which targeted data centers belonging to the Russian technology company Yandex, has cut down on spam and disinformation campaigns fomented by Russian bot farms. Social media users have claimed they've noticed less traffic on posts by MAGA-affiliated a … Read the full story at The Verge.

A comprehensive review argues that sustainable rice production depends on integrating microbiome engineering, synthetic microbial communities, precision agriculture, renewable energy and climate-smart policy into a single coordinated system.

China has unveiled a smart robot prototype designed to assist with wheat breeding in the latest step in its ongoing drive to advance agricultural technology and bolster food security. The robot made its public debut at a recent seminar on wheat breeding, state-run China Science Daily reported on Friday. Traditional wheat breeders rely on a wealth of accumulated experience to identify and select strains with superior traits from tens of thousands of hybrid offspring. But this requires manual...

A new drone-based study shows that ordinary cameras can map plastic film residues on farmland, while purpose-built short-wave infrared sensors could make soil plastic monitoring unambiguous.

arXiv:2610.11764v1 Announce Type: new Abstract: Reliable crop-row perception is essential for autonomous agricultural robots, but low-cost deployment is constrained by computation, memory, power, and inference time. This paper addresses crop-row detection and navigation-line extraction using UltraLight Luma, a compact encoder-decoder segmentation network with a progressive learning strategy and only 13.1k trainable parameters. The predicted crop-row masks are processed using a post-processing algorithm to estimate row alignment for downstream visual servoing. UltraLight Luma improves parameter efficiency over U-Net, YOLOv8, and YOLOv26 while maintaining reliable crop-row detection performance. The model required only 25.23 mJ per inference, demonstrating its suitability for resource-constrained agricultural robots.

arXiv:2610.08807v1 Announce Type: new Abstract: Precision pesticide spraying is essential for optimizing application efficiency and ensuring uniform chemical distribution. Spraying performance is influenced by multiple factors, including environmental conditions such as temperature and wind speed, pesticide type, and the robot's capability to accurately perceive crops and target spray locations. Existing approaches predominantly emphasize crop detection and rely on predefined spraying parameters, whereas human operators dynamically adjust their spraying strategies by considering environmental conditions, region-specific crop characteristics, and the type of pesticide being applied. In this study, we propose a context-aware adaptive spraying framework based on Vision-Language Models (VLMs), which enables robots to leverage spatial reasoning and integrate information from multiple sources, including crop type, pesticide type, and weather data, to make adaptive and optimized spraying

The fifth annual Farm Robotics Challenge will enable students from middle school through graduate school to enter agricultural automation projects and compete for prizes. The contest is held each year by the University of California and USDA.

arXiv:2610.08603v1 Announce Type: new Abstract: Early stress detection in crops is a necessity today to improve efficiency and reduce waste of time, money, and effort. However, most modern techniques, such as hyperspectral imaging and AI-based systems, are too costly and complex for medium and small-scale farmers to implement. This paper showcases CropSentry, a low-cost, ground-based multi-robot system that uses multimodal leaf sensing to continuously monitor crop health by tracking stress levels. The system comprises two autonomous bots that continuously detect leaf color and environmental data row by row. The observations are spatially mapped and sent over to the master bot, which uses color-coded row segments to generate a real-time web-based dashboard displaying crop health. After 63 observations were collected during the experiments, the results showed an overall crop health classification accuracy of 84.12%, with 82.60% for healthy plants, 88% for nutrient-deficient plants, and

A new deep learning framework reconstructs invisible Red Edge and near-infrared bands from ordinary RGB drone imagery, bringing multispectral crop monitoring within reach of smallholder cocoa farmers.

Robotic fruit picking has long promised to ease the labor crunch facing orchard growers, but the promise has repeatedly collided with a stubborn technical reality: a robot’s camera sees a chaotic world of overlapping leaves, dappled light, and fruit hanging at awkward angles. A study published in BMC Plant Biology by Hongyan Zhu, Runlong Cao, […]

AI-powered drones are transforming field monitoring, turning aerial data into actionable intelligence that helps agribusinesses prioritize inspections and improve efficiency. The post Beyond Crop Scouting: How AI-Powered Drones Could Reshape Precision Agriculture appeared first on CropLife.

A drone that decides, mid-flight, exactly where to spray herbicide and where to hold its nozzles has moved a step closer to the field. Researchers at the University of Arkansas System Division of Agriculture have built and tested an adaptive sprayer control system that mounts on an unmanned aerial vehicle (UAV), images the soybean canopy […]

The Farm Robotics Challenge launched its 2027 season on Sept. 15, unveiling a new three-division competition structure, a Startup pathway for teams committed to launching a company and more than $200,000 in prizes and startup support backed by a coalition of industry partners. Watch the 2027 Launch Webinar replay to learn about the new divisions, awards, Startup pathway and … The post Farm Robotics Challenge Launches 2027 Season appeared first on California Ag Network.

The post Waymo Robotaxi Finds Itself in the Middle of Denver’s Farmers Market appeared first on Car Revs Daily.

The crop robotics sector is a dynamic and active innovation environment, but also one that is extremely competitive for startups. The post The Mixing Bowl says it’s still ‘early days’ for crop robotics but AI, M&A are driving advancement appeared first on AgFunderNews.

New research in Plants, People, Planet demonstrates how precision agriculture techniques involving drones and sensor-based irrigation can save water needed to produce tomatoes and grapes while also improving the quality of these crops.

arXiv:2609.25725v1 Announce Type: new Abstract: Agricultural robotics is advancing rapidly, yet progress remains constrained by limited field access, lack of control over field conditions, geographic variability, and seasonal crop cycles. These factors make it difficult and costly to acquire diverse agricultural datasets, resulting in limited evaluation and reduced system robustness. While other robotics domains have scaled learning and evaluation through high-fidelity simulation, agricultural robotics still lacks comparably capable tools. In this paper, we present a ROS-integrated framework, built on Isaac Sim, for large-scale procedural generation of agricultural environments. The framework supports photorealistic rendering, physics simulation, and domain randomization at scales relevant to robotics research, with built-in support for row crops, orchards, and vineyards and straightforward extensibility to additional crop categories. Project Page: https://baj31415.github.io/agrigen/

X has sued Vivek Kumar Sen and Zmyang Sherpa for running fake bitcoin headline accounts to fraudulently earn creator rewards from the platform.

arXiv:2609.21059v1 Announce Type: new Abstract: Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent advances in frontier artificial intelligence have enabled scientific agriculture with strong potential to improve crop productivity. In this paper, we identify the importance and inherent complexity of plant shade simulation, as shading is a critical factor influencing plant growth. To advance this field and promote broader societal benefits, we focus on two main contributions. First, we introduce a comprehensive plant growth and shade dataset covering four plant species, including soybean, tomato, sugarbeet, and strawberry. The dataset includes top-down viewpoints with a supplementary light along a circular trajectory, casting dynamic shadows across multiple growth stages and diverse observation complexities. Second, we propose generative shade simulation based on diffusion models, enabling

Farmers with 10+ years of experience help provide insights into incentive program design, monetization, and management practices that drive returns on investment. The post 2026 National Cover Crop Report Shows Continued Expansion of Cover Crop Use, Including Cover Crop Grazing, Use of Drones, and Planting Patterns appeared first on CropLife.

arXiv:2609.20048v1 Announce Type: new Abstract: Herbicide-based weed control is increasingly unsustainable due to rising weed resistance and the adverse environmental impacts of chemical use. While mechanical weed control avoids these drawbacks, it is typically implemented using large machines that cause soil compaction. We propose a novel alternative based on small mobile robots for mechanical weeding. Compared with existing automated mechanical weeding approaches, the proposed method offers reduced soil compaction, simpler automation, and improved scalability. Our solution involves a Boston Dynamics Spot quadruped robot equipped with a custom weed removal tool featuring a milling bit at its end. The tool is rigidly attached to the robot and uses the degrees of freedom of the robot base by actuating the legs, while keeping the feet stationary. We develop a software architecture that enables autonomous weed removal and integrate this system with all other required components. We

arXiv:2609.19592v1 Announce Type: new Abstract: This study developed and evaluated a deep-learning-based perception framework for selective robotic cotton picking. The dataset contained 1,008 annotated field images collected using three cameras under varying natural lighting and weather conditions. Object-detection models from the YOLOv8 through YOLOv13 families were evaluated using their default configurations, while segmentation performance was assessed using YOLOv8-seg, YOLOv11-seg, YOLOv12-seg, the Segment Anything Model (SAM), SAMv2.1, FastSAM, and Grounded-SAM with the Recognize Anything Model (RAM). Among the detection models, GELAN-s achieved the most favorable balance between mean average precision (mAP) and inference speed, obtaining an mAP of 86.1%, precision of 81.6%, recall of 76.6%, and an F1-score of 79.0%, with an average inference time of 42.3 ms per image. Among the direct segmentation models, YOLOv12-m-seg provided the most favorable balance between AP@0.5 and FPS,

Norway's first fully automated facility for vegetable production was put into operation last fall. This spring, the first products hit store shelves.

King Charles has a long public record of engagement with Muslim communities and interfaith dialogue, but there is no substantiated evidence that he converted to Islam.

arXiv:2609.18738v1 Announce Type: new Abstract: Automating robotic harvesting in intensive agriculture within Mediterranean greenhouses requires overcoming significant challenges related to the geometric complexity of plants and occluded fruits. Although existing literature offers solutions targeting crops that grow in isolation (e.g., apples, sweet peppers, or peaches), the fundamental challenge lies in cluster-growing vegetables, where fixed sensors mounted on robotic systems fail to detect fruits hidden behind the visible surface. To address this limitation, this study presents a comprehensive pipeline for the 3D reconstruction and precise localization of each fruit within a cluster, including heavily occluded instances. The proposed methodology is structured into five sequential stages: i) point cloud acquisition using the AgriSEE Next Best View (NBV) active planner; ii) stochastic noise filtering via Statistical Outlier Removal (SOR); iii) surface classification and segmentation

arXiv:2609.18051v1 Announce Type: new Abstract: Fresh-market blueberries require selective, gentle picking, which is labor-intensive and expensive. Over-the-row machine harvesters are fast but non-selective, bruising mixed-ripeness fruit and limiting yield to the processing market. Selective robotic harvesters typically target individual fruits rather than fruit clusters, which limits harvesting efficiency for small, densely clustered blueberries. This paper presents CLASP, a Cluster-Level Autonomous Selective Picking robot with a Soft Active Rolling-Band Gripper (SARB-Gripper). Two compliant bands envelop the cluster and roll against the fruit, drawing mature berries off in sequence, while closed-loop regulation of the pulling force keeps the applied load below the immature detachment threshold. A global-to-local perception pipeline pairs an eye-to-hand camera for global cluster detection and target selection with an eye-in-hand camera for local localization and cluster orientation

arXiv:2609.15667v1 Announce Type: new Abstract: Agriculture faces many challenges, and robotic systems can play an important role in addressing them by improving the efficiency and sustainability of field operations. Among these challenges, preserving soil health is a critical concern, as vehicle-soil interactions can degrade the soil structure and produce unwanted surface deformation. A key step toward soil-aware robotics is to explicitly account for how vehicle traffic deforms the ground, yet soil state is typically not treated as a variable. We address this gap by proposing a framework to quantify traffic-induced soil deformation and estimate its evolution online from lidar observations. The method relies on a reduced-order parametric model that represents the soil behavior via physically interpretable parameters, yielding a continuously updated and observable representation of soil state. Experiments conducted in different soil conditions demonstrate the ability of the approach to

arXiv:2609.13606v1 Announce Type: new Abstract: Multi-arm robotic harvesting offers a promising path to improve harvesting efficiency and reduce reliance on manual labor. However, practical deployment remains challenging because the system must generalize across diverse environments while efficiently coordinating multiple arms in a shared workspace. Existing methods often require substantial data collection in target environments or rely on simplifying assumptions that limit planning quality. In this work, we introduce the first comprehensive benchmark for evaluating pretrained Vision-Language Models (VLMs) on zero-shot multi-arm fruit harvesting planning. Our benchmark uses real-world apple and citrus orchard images and compares a VLM-based planning pipeline with a traditional perception-and-planning pipeline. The VLM pipeline directly generates harvesting sequences and waypoints for each arm, while a lightweight trajectory verifier checks for collisions. Our results show that

A quiet revolution is unfolding across the world’s farmland, one articulated arm and autonomous wheel at a time. A new state-of-the-art review published in the International Journal of Intelligent Robotics and Applications takes stock of how robotics is reshaping agriculture, from the strawberry rows of Japan to the wheat belt of China, and delivers a […]

arXiv:2609.11766v1 Announce Type: new Abstract: Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced

arXiv:2609.11445v1 Announce Type: new Abstract: Reliable robot deployment requires online failure monitoring, yet existing monitors mainly derive risk from proxy signals or train dedicated monitoring components. We ask whether the internal predictive states of a frozen pretrained robotic world model already contain directly decodable failure information. Failure-Aware Readout from World Models (FARM) trains only a 33,985-parameter supervised readout over frozen VLA-JEPA predictive states, producing step-wise failure scores and causal trajectory risk. Five-fold out-of-fold evaluation across seven source tasks reaches 85.68/88.59 pooled AUROC/AUPRC, and FARM gives the best Seen performance among 15 matched baselines on the 10-task benchmark. Across four real-robot populations on PIPER X, SO-101, and Franka, fixed-readout transfer and readout-only adaptation test deployment shifts without updating the predictive backbone. FARM also discriminates failures from partial causal histories and

China’s GEAIR 2.0 humanoid robot pollinates tomato flowers in under 10 seconds, combining AI, robotics and gene editing for crop breeding and farm automation. The post China’s GEAIR 2.0 Robot Pollinates Crops in Under 10 Seconds appeared first on TechRepublic.

Greg Reverdiau, an Arizona-based drone trainer, said he saw something highly unusual for politically polarised America when he waded through public comments on a US government proposal to restrict foreign-made drones: a consensus. Reverdiau, the founder of Pilot Institute, which helps aviators obtain licences, said he reviewed over 3,800 comments submitted to the Federal Communications Commission (FCC) and estimates that about 98 per cent opposed the proposal or raised concerns about it. “Those...

High above the fields of the Morven Sustainability Lab, drones equipped with heat and light sensors are changing

Something strange is happening in the way humanity talks about its greatest crisis. While greenhouse gases accumulate in the atmosphere and global temperatures continue their relentless climb, researchers across multiple disciplines are documenting an equally significant phenomenon: the words, metaphors, and narrative frames we use to comprehend climate change are proving inadequate to the task. […]

arXiv:2609.03680v1 Announce Type: new Abstract: Labelling vision datasets, especially for segmentation tasks, is a laborious and costly process that stymies novel developments in agricultural robotics. In this paper, we present DropClick, a click-guided segmentation tool that simplifies the annotation process. Our system utilises single-click inputs on objects to generate pseudo-labels, which can replace manual annotations. DropClick stands out as it is a semi-automated approach and does not require a click for every object in the scene. It can therefore further reduce the required amount of user input drastically. We evaluate our method on two challenging agricultural robotic datasets, SB20 and BUP20 for plant and fruit segmentation, respectively. DropClick is first trained on a small subset of just 5 images from the original training data. This DropClick model can then be deployed as a one-click segmentation system and achieves comparable or higher performance than other one-click

Plus: Defra grans and more farmland funding around the world The post AgriFood Signals: Remembering Wendell Berry, Farmland LP’s new fund, Dairy Queen robots appeared first on AgFunderNews.

WEST LAFAYETTE, Ind. — Solinftec, a global leader in artificial intelligence and robotics for agriculture, will showcase the continued expansion of its Solix autonomous farming platform at the 2026 Farm Progress Show, Sept. 1–3 in Boone, Iowa. Following its largest U.S. commercial season to date, Solinftec will launch an Amazon store for Solix replacement parts, share new […] The post Solinftec to Launch Ag Robotics’ First Amazon Parts Store appeared first on Morning Ag Clips.

arXiv:2608.29237v1 Announce Type: cross Abstract: Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or technological resources. This paper presents the Automated Guided Robot for Insect and Crop Activity Monitoring (AGRICAM), a purpose-built robotic system designed to meet the requirements of large-scale pollination monitoring in protected cropping systems. AGRICAM operates autonomously on low-cost, easily installed track for movement along crop rows, without disrupting farm operations or insect behaviour. The platform integrates two RGB cameras, microclimate sensors, GPS and RFID modules, motion sensors, and 4G cellular network connectivity for data transmission. A web interface enables remote device configuration and

High above the fields of the Morven Sustainability Lab, drones equipped with heat and light sensors are changing what it means to farm smarter. This month, researchers and students from the University of Virginia, NASA and the University at Buffalo tested drone technology by flying more than 100 feet (30 meters) above crops to measure plant health at UVA's 2,900-acre (1,170-hectare) research facility.

arXiv:2608.27545v1 Announce Type: new Abstract: This study evaluates the effectiveness of remote human-robot interaction using virtual reality for leaf inspection and soil moisture assessment in a greenhouse environment. The robotic system comprised an unmanned ground vehicle and a robotic manipulator equipped with cameras, governed by kinematic models for navigation and manipulator control. Fourteen distinct plants were inspected across two experiments utilizing VR teleoperation, guided by a set of pre-specified research questions and hypotheses. In the leaf inspection experiments, cycle completion times varied from 3.3 to 8.0 s, and plant-based disease detection was achieved up to 88% accuracy; diseased-spot detection improved numerically in the second experiment, though this change was not statistically significant (p=0.378). For soil moisture assessment, the experiments achieved successful determination of watering needs in up to 64.3% of plants (9 of 14), with consistent success

Own the Robot or Hire It? The Two Very Different Roads Leading German Farmers Toward Autonomous Agriculture Autonomous field robots have been promised to agriculture for years: mobile machines that would prepare soil, sow seeds, weed rows, treat pests and even harvest crops under human supervision but without a human at the controls. On German […]

Advances in AI, perception and edge computing, plus more off-the-shelf tech, are helping startups build more flexible ag robots faster, says Reservoir founder Danny Bernstein. The post ‘The whole system is now more investable’: Reservoir makes the case for ag robotics appeared first on AgFunderNews.

X uncovered 200,000 inauthentic Chinese accounts pushing anti-AI and anti-data center narratives in the United States amid energy policy debates.

Solinftec expands Solix autonomy with autonomous refills, 85% chemical reductions, new models and Amazon parts access for farmers. The post Solinftec to Launch Ag Robotics’ First Amazon Parts Store as U.S. Solix Acreage Grows 15-Fold appeared first on CropLife.

The X Safety Team said that at least 200 bot accounts have been making posts to influence public opinion data centers and energy policy. The accounts share links to legitimate news stories and then add their own spin designed to inflame emotions.

X found a bot farm in its investigation of inauthentic Chinese accounts. Out of 200,000 bots, 200 allegedly focused on posting against AI data centers.

Earlier, a source in the judicial system said the Volgograd Regional Court had terminated the proceedings in the case of 66‑year‑old Nikolay Mukhin

arXiv:2608.18446v1 Announce Type: new Abstract: End-to-end imitation learning avoids hand-made robot motion for approaching and grasping, but the policy must still decide which fruit to pick and where to close the gripper. Occlusion can make the policy lose the selected fruit during harvesting, and the correct closing point is difficult to infer from pixels alone. This paper presents HarvestPoint-ACT, which makes both decisions explicit in perception and provides them to the policy. An instance segmentation front end with a keypoint branch predicts a mask and a harvest point for each visible fruit, where the harvest point specifies the location to close the gripper. A scheduler ranks detected candidates by occlusion and travel distance and selects one target. After each attempt, it redetects and reranks the candidates because the canopy may have changed. The selected fruit is encoded for an action chunking transformer as an eight-dimensional state, containing the absolute harvest

"The signals and data points that come from a company's participation in a field day are invaluable in vetting teams, and too many investors are missing them," says Connie Bowen at Farmhand Ventures. The post Guest article: The field day is the new term sheet; how growers are vetting ag robotics for real-use application appeared first on AgFunderNews.

In turn, the IHA agency reported the discovery of drone wreckage of unknown origin on the coast in Istanbul’s Arnavutkoy district

No casualties were reported, Penza Region Governor Oleg Melnichenko said

In the US, says Isaac Mazor, “manual citrus harvesting costs around $42–43 per 900-lb field bin, versus $25-30 using our technology.” The post Inside Nanovel’s bid to crack robotic citrus harvesting appeared first on AgFunderNews.

Monster Wolf and Pochi are among the robots deployed to scare away bears and transport cherry blossoms in a northern region where many farmers are in their 70s or older.

Monster Wolf and Pochi are among the robots deployed to scare away bears and transport cherry blossoms in a northern region where many farmers are in their 70s or older.

arXiv:2608.02270v1 Announce Type: new Abstract: Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agriculture. Meanwhile, a growing number of retired low-speed electric-vehicle (LSEV) powertrains retain functional electromechanical value but are destructively recycled. This paper presents TS-MAMP (Telescopic-Sleeve Modular Agricultural Mobile Platform), a remanufactured robot built under 3R (reduce, reuse, recycle) circular-economy principles. Retired 48 V brushless-DC (BLDC) hub motors are paired via back-EMF matching, and lead-acid battery modules screened at 60%-80% state of health are actively balanced within a 100 mV inter-module voltage deviation. Together, these reused components reduce the powertrain-and-chassis BOM cost by approximately 60%, to below USD 450 (perception and weeding modules excluded). The truss chassis provides >=200 kg static load, continuously adjustable track width

A £20 million competition has opened to develop farm robots and automated technology for crops, livestock, horticulture and forestry. Agr...

To offset an ageing agricultural workforce and boost sustainable yields, China is aggressively accelerating the adoption of farm drones – a trend that analysts say could spread to more emerging markets despite trade restrictions imposed by the United States. Industry leader DJI said its global sales of agricultural drones had exceeded 700,000 units across China and more than 100 other countries and regions by the end of last month – more than a fivefold increase in just five years. Use of the...

China’s agriculture sector needs more advanced technology and innovation to boost yields and improve food security – not more farmland and crops, according to a rural policy expert. The remarks came after a state media investigation alleged that officials in central China had inflated the size of mandatory rice crops to hit their targets. CCTV programme Focus Report aired its investigation on July 14, claiming officials in Jianghua Yao – a county in Yongzhou, Hunan province – had misreported the...

MANHATTAN, Kan. — As Kansas wheat harvest wraps up, producers are leaving the field with something just as valuable as the grain in their trucks: data that can drive smarter decisions for years to come. Deepak Joshi, a precision agriculture specialist at Kansas State University, said yield data collected by combine harvesters is one of […] The post Data and Drones Helping Farmers Squeeze More From Every Acre appeared first on Morning Ag Clips.

An electric, modular robot equipped with sensors, capable of moving non-invasively through orchards and vineyards and detecting crop diseases, is now available to farmers and agricultural producers.

Most autonomous-systems research happens in controlled or semi-controlled environments, and that framing matters more than it first appears.

Andrew James (from left), Neil Morrison, Natalia Kurz and Michael Neiss work on a prototype of their weed-killing robot ahead of The Farm Robotics Challenge, which they won on May 21. By Holly Hartigan A team of Cornell undergraduates beat 95 other teams to take the grand prize at The Farm Robotics Challenge with their […]

A pair of Midwestern farmers say the wet spring and summer has them keeping an eye out for crop diseases. Thomas Perkins started farming in central Illinois in Effingham County in 2016. “We’ve already applied fungicide with the drone on some of our early, early planted corn.”  He says, “We’re starting to see a little […] The post Farmers finding value in fungicides and drones appeared first on Brownfield Ag News.

arXiv:2607.12065v1 Announce Type: new Abstract: While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions. In fact, deploying autonomous robots at night offers significant advantages, including 24-hour crop and soil monitoring, fruit harvesting, and nocturnal pest detection. Modern vision-based systems, however, rely heavily on large-scale well-annotated image datasets, which remains challenging to obtain for nighttime operation scenarios. To address this, we propose an unsupervised image translation framework that converts daytime plant-row RGB images into near-infrared (NIR) nighttime counterparts without requiring pixel-to-pixel supervision. This enables the direct reuse of daytime semantic labels for training nighttime perception models. In particular, by incorporating a pre-trained Contrastive Language-Image Pre-training (CLIP) model, the proposed framework is designed to preserve semantic consistency during

Plus: FMC's new herbicide The post AgriFood Signals: Chipotle’s next agrifood cohort, John Deere settlement, fruit-harvesting robots appeared first on AgFunderNews.

The director of conservation agriculture and farm operations at Missouri Soybeans says the future of farming includes more robotics, precision agriculture and innovative technologies designed to improve efficiency. Clayton Light tells Brownfield “Obviously robotics are up and coming, very promising, but it’s going to take a while for it to get there. Definitely the drones […] The post Missouri Soybeans: robotics, precision ag and innovation are shaping the future of farming appeared first on Brownfield Ag News.

AMRUT Drone Mission and Salam Kisan partnered to connect trained drone pilots with farmers, expanding precision agriculture, improving input efficiency, creating rural livelihoods, and accelerating drone adoption across Maharashtra.

arXiv:2607.06337v1 Announce Type: new Abstract: Robotic tree-fruit harvesting is a flagship problem for agricultural automation, but progress is bottlenecked by the cost and irreproducibility of field experiments: an orchard is available only weeks a year, every tree is different, and a control error can permanently damage the crop or the plant. The tree models used in graphics and agronomy are geometrically detailed but physically inert, while the GPU-parallel simulators used in robot learning contain no plausible trees. We present OrchardBench, a physically-grounded, GPU-parallel simulation of apple-orchard trees on the Newton engine. Each tree is grown by a stochastic L-system and instantiated as a fully articulated body: branches are compliant torsional spring-dampers whose stiffness follows Euler-Bernoulli beam theory, they break at a wood modulus of rupture and fall as free hinges, and apples are independent bodies on stem tethers that detach at literature-grounded pull forces

A crop protection specialist says spray drones can play an important role on today’s farms. WinField United’s Tyler Steinkamp tells Brownfield, “Drones, especially as we continue to develop this technology, they just keep getting better and better.” He says growers can use them for a variety of crop protection applications. “Any pesticide that is currently […] The post Are drones a useful tool for farming operations? appeared first on Brownfield Ag News.

A report suggests that upcoming nuclear waste-powered radiovoltaic batteries could last as long as 30 years and power next-gen drones.

Iran deal tested by drone attack as Trump offers up US farm goods  Fox NewsSee more headlines & perspectives on Google News

Iran deal tested by drone attack as Trump offers up US farm goods  Fox NewsSee more headlines & perspectives on Google News

ITHACA, N.Y. — A team of Cornell undergraduates beat out 95 other teams to take the grand prize at The Farm Robotics Challenge with their invention: an autonomous robot that kills weeds with electricity. Their robot can travel through a vineyard or orchard without a human operator, zapping weeds with a small amount of electricity, […] The post Undergrads’ Weed-Killing Robot Wins Top Prize appeared first on Morning Ag Clips.

The role of drones in crop protection is growing. Tyler Steinkamp is the crop protection product manager for WinField United. He tells Brownfield more growers are leveraging drone technology to spray their fields. “Farmers, applicators, we’re attracting a lot of people to the ag industry having these spray drones out there,” he said. “It’s something […] The post Spray drones gaining ground in crop protection appeared first on Brownfield Ag News.

Robotic 'cobots' could help English soft fruit growers move harvested crops faster, ease labour pressure and reduce waste under a new Innova...

arXiv:2606.20239v1 Announce Type: new Abstract: Drones are increasingly used in agriculture, where tight margins demand efficient planning. Current optimization tools suffer from exponential runtimes as problem sizes grow, necessitating practical heuristics for daily operations. This paper presents an operational framework and benchmarking analysis for drone spraying operations. We evaluate the trade-offs between facility siting methods and tiered routing parameters. For facility siting, comparing a Mixed-Integer Program (MIP) baseline against a $p$-Median heuristic shows that the heuristic reduces runtime by three orders of magnitude, from over 97 seconds to under 1.2 seconds, with only a 4\% reduction in serviced field area. For route planning, a tiered problem decomposition approach partitioning the target area into 6 to 8 spatial clusters reduces computation time by an order of magnitude with minimal degradation in serviced area. This framework achieves minute-scale planning on

arXiv:2606.11381v2 Announce Type: replace Abstract: Robotic strawberry harvesting requires precise 6D pose estimation; however, collecting 6D pose ground truth in real agricultural fields is inherently challenging. Existing strawberry 6D pose estimation studies have therefore relied mainly on synthetic data, often without sufficient scene-level realism,leaving their performance under real agricultural field conditions unquantified. In this work, we present, to the best of our knowledge, the first real-world 6D pose ground truth dataset of strawberries collected in actual agricultural fields (12,040 images). We also introduce a synthetic dataset rendered in NVIDIA Isaac Sim, featuring scene-level realism and domain randomization. Despite this improved simulation setup, our experiments reveal that a substantial sim-to-real gap persists, underscoring the necessity of real agricultural field data for reliable evaluation. We further quantify the sim-to-real gap through baseline 6D pose

arXiv:2606.14089v1 Announce Type: new Abstract: Robotic apple harvesting offers a promising solution to labor shortages in commercial orchards, but low throughput and poor performance in orchard environments hinder its commercial adoption. This paper presents a modular dual-arm apple harvesting robot that uses a vertically stacked arms to enable simultaneous operation in the upper and lower zones of a single tree, simplifying platform positioning from multi-tree lateral repositioning to single-tree stops. Compared to our prior horizontal dual-arm system, the platform integrates 5 advances: (1)a foundation-model-based perception pipeline combining Grounding-DINO and EfficientViT-SAM for robust fruit localization in unstructured outdoor environments; (2)7th-order jerk-bounded trajectory generation paired with a Control Barrier Function safety filter to achieve fast yet safe arm motions; (3)a linear sweep harvesting strategy with a 10cm approach buffer and rotational detachment that

MANHATTAN, Kan. — Drones are becoming an increasingly important tool for farmers seeking to improve efficiency and crop management, according to a Kansas State University precision agriculture expert. Deepak Joshi, a faculty member in K-State’s Department of Agronomy, said the growing use of drones in early-season crop scouting can help producers quickly assess plant germination, […] The post K-State Expert Says Drones Can Improve Crop Scouting, Reduce Labor appeared first on Morning Ag Clips.

MADISONVILLE, Ky. — A two-day drone pilot certification workshop and exam are being offered July 8-9 by the Kentucky Agriculture Training School (KATS), a program of the University of Kentucky Martin-Gatton College of Agriculture, Food and Environment. According to KATS coordinator Lori Rogers, the workshop will prepare candidates for the certification required under the Federal Aviation Administration (FAA) Small […] The post FAA Certification Workshop and Exam for Agricultural Drone Pilots appeared first on Morning Ag Clips.

arXiv:2606.10971v1 Announce Type: new Abstract: Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency vibrations inherent in off-road environments. This paper proposes a robust navigation algorithm based on a jerk-augmented Extended Kalman Filter (EKF) integrated with a Multiple Tuning Factor (MTF) adaptation method. Unlike standard EKF approaches that assume constant measurement noise, our method dynamically adjusts the measurement covariance matrix in real-time, allowing the system to cope with sudden disturbances and sensor outliers. We evaluate the algorithm using real-world data from a Salin247 autonomous robot. Results demonstrate that jerk-augmentation combined with MTF adaptation significantly reduces 3D position Root Mean Square Error (RMSE) compared to baseline EKF models, providing superior dead-reckoning capabilities.

ONLINE — On the Uplevel Dairy Podcast, Peggy sits down with Sam Fessenden, a Cornell dairy science graduate with a PhD focused on the CNCPS model who worked with nutritionists globally before partnering with his wife Brenda and her parents, Craig and Cathy, to rebuild dairying at their Southeast Minnesota site. In this conversation, Sam […] The post Robots, Faith and Resilience: Transforming Tradition at Silver Spirit Farm appeared first on Morning Ag Clips.

A robot designed to detect and remove poisonous ragwort before it threatens livestock is being tested on farms across Dorset. The autonom...

We asked three farmers to tell us how new technology is revolutionizing the way they work.

We asked three farmers to tell us how new technology is revolutionizing the way they work.

We asked three farmers to tell us how new technology is revolutionizing the way they work.

Reservoir's Danny Bernstein discusses the evolution of farm robotics to multi-task, multi-crop machines and the role of VC in this space. The post Reservoir’s Danny Bernstein says we’re ‘entering the golden age of robotics’ in agriculture appeared first on AgFunderNews.

Researchers at Osaka Metropolitan University have developed a method for creating realistic virtual tomato farms that automatically generate data for training agricultural AI systems. Their approach offers a way to overcome one of the most labor-intensive tasks in farming: harvesting the crops.

arXiv:2605.28883v1 Announce Type: new Abstract: Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this deforestation contributes to climate change. This paper proposes a novel logging method for tropical forests, Ultra-Reduced-Impact-Encased-Logging (URIEL). This new method is based on heli-logging techniques combined with intensive use of robotics and AI integrated with post-harvest silvicultural treatments performed by drones. The concept of appropriate equipment for this method was developed, dimensions were determined, details were completed in a digital proof of concept, and an effective digital simulation and economic feasibility analysis were carried out for various helicopter-timber-distance combinations. The results demonstrated that a URIEL method has high economic viability and makes it possible to virtually eliminate collateral damage to forests while maintaining ecosystem

The potential for tariffs and an outright ban on Chinese drones is weighing heavily on ag users. The post Drones Unmade in China: Proposed U.S. Ban Clouds Future of Agricultural Spray Drone Industry appeared first on CropLife.

arXiv:2605.27129v1 Announce Type: new Abstract: In greenhouse tomato production, automated harvesting requires accurate detection of ripe tomatoes, ripeness classification, and precise picking-point localization for robotic end-effectors. This paper proposes YOLO26-RipeLoc Lite, a lightweight deep learning architecture based on YOLO26 for simultaneous detection, ripeness classification, and center-point localization of greenhouse tomatoes. The model introduces three modifications: (1) a Lightweight Feature Pyramid Network (LFPN) with depthwise separable convolutions for efficient multi-scale fusion, (2) a Ripeness-Aware Attention Module (RAAM) with dual pooling and a learnable ripeness bias vector for enhanced color-texture discrimination, and (3) a Compact Detection Head (CDH) with shared convolutions and an integrated center-point regression branch for direct grasp planning. The model is evaluated on a custom dataset of 1,500 images with 6,227 instances (3,566 ripe, 2,661 unripe)