The paper introduces an autonomous robotic platform that performs targeted chlorophyll fluorescence measurements on plant leaves. It integrates 3D plant reconstruction, geometric analysis, and motion planning to identify suitable leaf surfaces and generate collision‑free trajectories for a robotic manipulator. This system enables automated, repeatable, and spatially resolved physiological measurements that extend beyond passive imaging.
By Ayman Laaroussi, Peter Hanappe, David Colliaux
arXiv:2608.23636v1 Announce Type: new
Abstract: Small-object detection and instance segmentation remain challenging in orchard environments because of green-on-green similarity, occlusion, and limite...
By Ranjan Sapkota, Manoj Karkee
The paper introduces a lightweight multimodal vision‑language framework based on TinyCLIP for fine‑grained classification of early‑stage apple fruitlet anatomy (calyx, fruitlet body, peduncle) in orchard images. Using a dataset of 600 high‑resolution RGB images, the model employs domain‑specific language prompts and a sliding‑window inference strategy to produce interpretable heatmaps for whole‑image localization. Achieving macro‑F1 of 0.93 on an NVIDIA T4 GPU and maintaining accuracy after INT8 quantization, the system is optimized for edge deployment on NVIDIA Jetson hardware with model sizes around 127‑137 MB and millisecond‑level inference.
By Ranjan Sapkota, William Bu, Chen Chen, Yunjun Xu, Manoj Karkee
The paper compares Ultralytics YOLO27, YOLO26, YOLO11, and YOLOv8 for detecting and segmenting small fruit parts in orchard settings. It evaluates five model scales across 30 experiments, finding that YOLO11s-960 and YOLO26s-960 achieve the best mask and box mAP scores while maintaining efficient parameter counts. The study also highlights the difficulty of peduncle detection and provides publicly available code and models for reproducibility.
By Ranjan Sapkota, Manoj Karkee
The paper presents a deep‑learning perception framework for selective robotic cotton harvesting, evaluated on 1,008 field images captured under diverse lighting and weather conditions. Detection models from YOLOv8 to YOLOv13 were benchmarked, with GELAN‑s achieving the best trade‑off between accuracy and speed. For segmentation, YOLOv12‑m‑seg outperformed other models, and a detection‑prompted segmentation approach using GELAN‑s bounding boxes further improved localization for SAM variants. Field trials with a UR5e robot and ZED2i camera confirmed YOLOv12‑m‑seg’s real‑time performance for cotton boll detection, segmentation, and selective picking.
By Thevathayarajh Thayananthan, Xin Zhang, Isuru Laddusinghe Badu, Jonathan Harjono, Glen C. Rains, Beiwen Li, Leonardo M. Bastos, Nuwan K. Wijewardane, Vitor S. Martins
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 i...
By Malika Nisal Ratnayake, Adel N. Toosi, James Cook, Romina Rader, Alan Dorin
arXiv:2608. 01202v1 Announce Type: cross Abstract: Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management.
By Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabija\'nska
arXiv:2609.24906v1 Announce Type: cross
Abstract: Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of m...
By Abhinav Jain, Cindy Grimm, Stefan Lee
arXiv:2609.13606v1 Announce Type: cross
Abstract: Multi-arm robotic harvesting offers a promising path to improve harvesting efficiency and reduce reliance on manual labor. However, practical deploym...
By Vrishan Inukollu, Adyan Zaman, Anvi Kudaraya, Carlos Lazcano, Yuankai Zhu, Stavros Vougioukas, Xiaofan Yu
Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques.
arXiv:2601. 02085v3 Announce Type: replace-cross Abstract: Strawberry-harvesting robots faced challenges such as poor visual perception, gripper misalignment, empty grasp/misgrasp, and slippage, which reduced harvesting stability and efficiency.
By Meili Sun, Chunjiang Zhao, Lichao Yang, Hao Liu, Shimin Hu, Ya Xiong
arXiv:2603. 07866v3 Announce Type: replace-cross Abstract: Offshore inspection and maintenance have increasingly been using legged robots for routine sensing, yet many useful interventions still require physical interaction with tools, containers, and task-relevant objects.
By Dilermando Almeida, Juliano Negri, Guilherme Lazzarini, Thiago H. Segreto, Ranulfo Bezerra, Gustavo J. G. Lahr, Ricardo V. Godoy, Marcelo Becker