arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
By Junxiong Zhou, Xuechen Li, Chonghao Qiu, Lang Qiao, Xiaowei Jia, Qi Yang, Chishan Zhang, Leikun Yin, Nanshan You, Vipin Kumar, David Mulla, Ce Yang, Zhenong Jin, Licheng Liu
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 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
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
arXiv:2609.13551v1 Announce Type: new
Abstract: Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-...
By Ivica Dimitrovski, Vlatko Spasev, Ivan Kitanovski, Petre Lameski, Dane Boshev
3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion.