agricultural robot developers

Abstract: Weed detection is an important component of precision agriculture, enabling site-specific weed management and reducing unnecessary herbicide use. Although deep learning methods have achieved strong results for crop and weed detection, many studies rely on single-dataset evaluation, making it difficult to assess robustness across different agricultural domains. This paper presents a multi-dataset benchmark of deep object detectors for weed detection in precision agriculture, with a focused evaluation of YOLO26 models. We evaluate nano, small, and medium variants on seven public weed-detection datasets covering different crops, weed species, field conditions, acquisition setups, and annotation protocols. The models are compared in terms of detection accuracy, model complexity, inference latency, FPS, and model size. In addition to in-dataset evaluation, we investigate cross-domain generalization using a unified one-class weed setup and evaluate multi-source training using the combined training subsets from all datasets. The results show that YOLO26 achieves strong in-dataset performance, with YOLO26m obtaining the highest average accuracy and YOLO26s providing the best practical accuracy-efficiency trade-off. However, cross-domain performance decreases substantially, with YOLO26s dropping from an average in-domain mAP$_{50:95}$ of 0.603 to 0.148 in the off-domain setting. Multi-source training improves performance on several datasets, but does not fully eliminate domain shift. Overall, the benchmark highlights the importance of dataset diversity, domain similarity, and target-domain adaptation for robust weed detection in real-world precision agriculture applications.

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/

Abstract: This paper presents a real-time semantic world modeling framework specialized for precision agriculture using autonomous robots. The framework combines probabilistic mapping of objects and their semantic attributes, updated through Bayesian inference, with a graph-based Simultaneous Localization and Mapping (SLAM) approach implemented using $g^2o$, a general framework for graph optimization. This integration enables accurate mapping and localization without relying solely on GPS. By leveraging semantic information such as plant type, size, and health, the robot can perform tasks while mapping and localizing itself within a field of crops. The proposed framework was validated through Gazebo simulations and physical experiments on an indoor field with artificial plants using Boston Dynamics' robot dog Spot. A YOLOv8n object detection model was trained to extract object and semantic data from depth camera observations. These simulations and experiments demonstrate that the system can successfully perform real-time mapping of up to at least 400 plants.

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 using Region Growing (RG); iv) isolation and recovery of occluded fruits through Density-Based Spatial Clustering of Applications with Noise (DBSCAN); and v) 3D pose estimation (position and orientation). This approach extracts the complete cluster geometry, ensuring the reliable identification of partially hidden tomatoes. Evaluated across multiple scenarios with varying occlusion levels within a simulation framework rigorously validated against real-world conditions, the system achieves a precision exceeding 90\%, an average recall of 82.8\%, and a mean Intersection over Union (mIoU) of 80.7\%. Furthermore, it demonstrates high repeatability in centroid estimation with a Root Mean Square Error (RMSE) of merely 4.2~mm, verifying its technical feasibility and high accuracy for autonomous harvesting operations.