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.
arXiv:2606. 02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural management.
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
The paper introduces a dataset‑centric benchmark for deep learning approaches to grape leaf disease classification and detection. It evaluates publicly available datasets on disease categories, annotations, acquisition conditions, and class distributions, and tests representative models across image‑level classification, region‑level classification, and object detection. Results reveal high accuracy on controlled datasets but significant performance drops on heterogeneous, real‑world data, especially in cross‑dataset transfer and object detection tasks.
arXiv:2606. 20438v1 Announce Type: new Abstract: Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology.
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...
arXiv:2105. 03358v4 Announce Type: replace-cross Abstract: In clinical applications, neural networks must focus on and highlight the most important parts of an input image.
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.
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.
arXiv:2405. 07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques.
arXiv:2606. 14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers.
arXiv:2508. 17254v2 Announce Type: replace-cross Abstract: Higher levels of machine intelligence demand alignment with human perception and cognition.
arXiv:2608.22368v1 Announce Type: new Abstract: While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the S...