Agricultural robots have emerged as powerful members in agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement, resulting in limited mobility and poor adaptability. Vision-and-Language Navigation (VLN) enables robots to navigate to the target destinations following natural language instructions, demonstrating strong performance on several domains. However, none of the existing benchmarks or methods is specifically designed for agricultural scenes. To bridge this gap, we propose A griculture to A griculture ( A2A) benchmark, containing 1,560 episodes across six diverse agricultural scenes, in which all realistic RGB videos are captured by front-facing camera on a quadruped robot at a height of 0.38 meters, aligning with the practical deployment conditions. Meanwhile, we propose V ision-and-L anguage Navigation for Agricultural Robots ( AgriVLN) baseline based on Vision-Language Model (VLM) prompted with carefully crafted templates, which can understand both given instructions and agricultural environments to generate appropriate low-level actions for robot control. When evaluated on A2A, AgriVLN performs well on short instructions but struggles with long instructions, because it often fails to track which part of the instruction is currently being executed. To address this, we further propose Subt ask List ( STL) instruction decomposition module and integrate it into AgriVLN, improving Success Rate (SR) from 0.31 to 0.42. We additionally compare AgriVLN with several existing VLN methods, demonstrating the state-of-the-art performance in the agricultural domain.

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety assurance framework can also be used during implementation, and subsequent deployment and operation phases.

We introduce a domain-specific reward mechanism that maximizes yield recovery while minimizing chemical usage by effectively handling noisy infection data and enforcing physical field constraints via action masking. We conduct a rigorous empirical evaluation across diverse, realistic biotic stress scenarios, capturing varying infection distributions and severity levels in row-crop fields. The proposed scheme is evaluated thoroughly, showing the framework's effectiveness and robustness. Experimental results demonstrate that our approach significantly reduces non-target spraying, chemical consumption, and operational costs compared to baseline methods. Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree Mahsa Khosravi a, Zhanhong Jiang b, Joshua R Waite b, Sarah Jones c, Hernan Torres c, Arti Singh c, Baskar Ganapathysubramanian b, Asheesh Kumar Singh c, Soumik Sarkar b a Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa, USA b Department of Mechanical Engineering, Iowa State University, Ames, Iowa, USA c Department of Agronomy, Iowa State University, Ames, Iowa, USAAbstract This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture.

In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape bunches, and applies it to agricultural robots. The Yolov3 detection network is used to realize the accurate detection of grape bunches, and the local tracking algorithm is added to eliminate relocation. Then it obtains the accurate 3D spatial position of the central points of grape bunches using the depth distance and the spatial restriction method. Finally, the counting of grape bunches is completed. It is verified using the agricultural robot in the simulated vineyard environment. The project code is released at: https://github.com/XuminGaoGithub/Grape_bunches_count_using_robots.

Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement.

Clothing for robots can help expand a robot's functionality and also clarify the robot's purpose to bystanders. In studying how to design clothing for robots, we can shed light on the functional role of aesthetics in interactive system design. We present a case study of designing a utility belt for an agricultural robot. We use reflection-in-action to consider the ways that observation, in situ making, and documentation serve to illuminate how pragmatic, aesthetic, and intellectual inquiry are layered in this applied design research project. Themes explored in this pictorial include 1) contextual discovery of materials, tools, and practices, 2) design space exploration of materials in context, 3) improvising spaces for making, and 4) social processes in design. These themes emerged from the qualitative coding of 25 reflection-in-action videos from the researcher. We conclude with feedback on the utility belt prototypes for an agriculture robot and our learnings about context, materials, and people needed to design successful novel clothing forms for robots.

If you look at the UN Sustainable Development Goals, it's clear that robots have a huge role to play in advancing the SDGs. However the field of Sustainable Robotics is more than just the application area. For every application that robotics can improve in sustainability, you have to also address the question – what are the additional costs or benefits all the way along the supply chain. What are the'externalities', or additional costs/benefits, of using robots to solve the problem. Solving our economic and environmental global challenges should not involve adding to the existing problems or creating new ones.

With the increasing deployment of agricultural robots, the traditional manual spray of liquid fertilizer and pesticide is gradually being replaced by agricultural robots. For robotic precision spray application in vegetable farms, accurate plant phenotyping through instance segmentation and robust plant tracking are of great importance and a prerequisite for the following spray action. Regarding the robust tracking of vegetable plants, to solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel method of Multiple Object Tracking and Segmentation (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants. In our approach, contour and blob features are extracted to describe unique feature of each individual vegetable, and associate the same vegetables in different images. By assigning a unique ID for each vegetable, it ensures the robot to spray each vegetable exactly once, while traversing along the farm rows. Comprehensive experiments including ablation studies are conducted, which prove its superior performance over two State-Of-The-Art (SOTA) MOTS methods. Compared to the conventional MOTS methods, the proposed method is able to re-identify objects which have gone out of the camera field of view and re-appear again using the proposed data association strategy, which is important to ensure each vegetable be sprayed only once when the robot travels back and forth. Although the method is tested on lettuce farm, it can be applied to other similar vegetables such as broccoli and canola. Both code and the dataset of this paper is publicly released for the benefit of the community: https://github.com/NanH5837/LettuceMOTS.

Agriculture has always remained an integral part of the world. As the human population keeps on rising, the demand for food also increases, and so is the dependency on the agriculture industry. But in today's scenario, because of low yield, less rainfall, etc., a dearth of manpower is created in this agricultural sector, and people are moving to live in the cities, and villages are becoming more and more urbanized. On the other hand, the field of robotics has seen tremendous development in the past few years. The concepts like Deep Learning (DL), Artificial Intelligence (AI), and Machine Learning (ML) are being incorporated with robotics to create autonomous systems for various sectors like automotive, agriculture, assembly line management, etc. Deploying such autonomous systems in the agricultural sector help in many aspects like reducing manpower, better yield, and nutritional quality of crops. So, in this paper, the system design of an autonomous agricultural robot which primarily focuses on weed detection is described. A modified deep-learning model for the purpose of weed detection is also proposed. The primary objective of this robot is the detection of weed on a real-time basis without any human involvement, but it can also be extended to design robots in various other applications involved in farming like weed removal, plowing, harvesting, etc., in turn making the farming industry more efficient. Source code and other details can be found at https://github.com/Dhruv2012/Autonomous-Farm-Robot

China's first artificial intelligence agricultural robot has officially started round-the-clock inspections in the smart vegetable and fruit greenhouse of the China-Israel Demonstration Farm in the Haixia Modern Agriculture Demonstration Park of the Fujian Academy of Agricultural Sciences, marking that the Fujian artificial intelligence agricultural robot has officially entered the practical application from the research and development stage. Seen in the Israeli demonstration farm, this robot looks like a white cartoon character with clear facial features and body. It can complete 360-degree rotation and movement through the wheels at the bottom and smoothly along the cultivation gutters, automatic inspection, fixed-point collection, automatic turning, automatic return, automatic charging, and automatic detouring if encountering obstacles on the way. "We have developed integrated multi-channel sensor technology for this robot so that it has human-like facial features." Zhao Jian, deputy director of the Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, said.