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. 11053v1 Announce Type: cross Abstract: The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming.
By Ismail Ismail Tijjani, Sunusi Muhammad Ibrahim, Amina Ibrahim Khaleel, Lanre Olusegun Akinola, Fatima Isa Jibrin, Muhammad Bashir Aliyu, Abdullahi Abdussalam Dalhat, Abdullahi Suiudeen
arXiv:2409. 16808v3 Announce Type: replace-cross Abstract: Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices.
By Daghash K. Alqahtani, Muhammad Aamir Cheema, Maria A. Rodriguez, Adel N. Toosi
arXiv:2608.21454v1 Announce Type: new
Abstract: The same fruit appears in a bunch, unpicked, peeled, bagged in plastic, or sliced on a dish, so automated fruit classification in the wild (AFCW) must...
By Subhankar Chattoraj, Sawon Pratiher, Samiran Das, Hubert Konik
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale.
The paper evaluates how well Vision Transformers (ViTs) can handle token merging techniques—specifically ToMe and Mutual Pair Merging—across wheat phenotyping tasks such as growth-stage classification, wheat-head detection, and wheat-organ segmentation. It benchmarks task quality, throughput, token count, and GPU memory usage, including tests on a Raspberry Pi 5. Results show that classification is highly tolerant to token merging, whereas detection and segmentation suffer due to factors like repeated instances, thin organs, dense boundaries, and runtime overhead, and that optimized attention backends can negate apparent speed gains.
By Simon Rav\'e, Pejman Rasti, David Rousseau
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
By Glenn Jocher, Jing Qiu, Mengyu Liu, Shuai Lyu, Fatih Cagatay Akyon, Muhammet Esat Kalfaoglu
arXiv:2512. 18046v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone detection systems.
By Ami Pandat, Punna Rajasekhar, Gopika Vinod, Rohit Shukla
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
arXiv:2606. 12218v1 Announce Type: cross Abstract: Understanding spatial distribution of fallow land is important for optimizing the food-water (FW) nexus, given fallowing's role in crop rotation and water conservation.
By Sk Muhammad Asif, Orhun Aydin
The paper presents the first real‑world 6D pose ground‑truth dataset for red‑stage strawberries, collected from 12,040 images at an actual farm using indirect camera pose recovery and 3D bounding‑box annotation. It also introduces a synthetic dataset rendered in NVIDIA Isaac Sim with scene‑level realism and domain randomization. Experiments show that models trained solely on synthetic data do not transfer well to in‑field images, but adding a small amount of real data significantly improves both translation and rotation accuracy across various backbone encoders.
By Woojung Son (Department of Agricultural and Biological Engineering, University of Florida), Won Suk Lee (Department of Agricultural and Biological Engineering, University of Florida), Zijing Huang (Department of Agricultural and Biological Engineering, University of Florida), Daeun Choi (Department of Agricultural and Biological Engineering, University of Florida), Catia Silva (Department of Electrical and Computer Engineering, University of Florida), Yu She (Edwardson School of Industrial Engineering, Purdue University), Yan Gu (School of Mechanical Engineering, Purdue University)
STA‑Net is a lightweight neural network designed for plant disease classification on edge devices. It combines a training‑free neural architecture search (DeepMAD) to build an efficient backbone with a novel Shape‑Texture Attention Module (STAM) that separates shape and texture processing using deformable convolutions and a Gabor filter bank. On the CCMT plant disease dataset, STA‑Net achieved 89.00% accuracy and 88.96% F1 score with only 401K parameters and 51.1M FLOPs.
By Zongsen Qiu, Jianjun Wang, Yue Zhou, Zibo Zhou, Rui Chen