arXiv Computer Vision
Enhancing Shrimp Disease Detection via Deep Learning and Data Refinement for Resilient Aquaculture
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arXiv AI
Jun 15
CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
arXiv Computer Vision
Aug 24
A Dataset-Centric Benchmark of Deep Learning Methods for Grape Leaf Disease Classification and Detection
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 Computer Vision
2d ago
FiVOS: A Fish Segmentation Algorithm Based on Interactive Video Object Segmentation and Filter Enhancement
arXiv:2610.01480v1 Announce Type: new Abstract: With the continuous expansion of aquaculture, precise and efficient monitoring of fish behavior has become increasingly critical for improving farming...
arXiv AI
Aug 13
Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification
arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
arXiv AI
Sep 1
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
arXiv:2603.02465v2 Announce Type: replace-cross Abstract: Machine learning-based wildfire detection has advanced significantly using deep learning models trained on large wildfire image and video dat...
Hugging Face Trending Papers
Jun 4
Comparison of Deep Learning Frameworks For Rice Disease Mapping From UAV Multispectral Imaging
In this study, UAV multispectral imagery is used to segment the severity of bacterial leaf blight (BLB) in rice using convolutional neural networks (CNNs) and transformer-based models. The evaluated architectures include U-Net with a ResNet- 101 encoder, U-Net++ with EfficientNet-B3 and EfficientNetB7, DeepLabV3+, and SegFormer, all trained under a common pipeline with three input configurations (multispectral only, multispectral+NDVI, and multispectral+NDRE).