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: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:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
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...
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).
Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures, ordinal label structure, and a small number of training samples.
arXiv:2608. 12230v1 Announce Type: cross Abstract: Non-destructive food quality assessment has increasingly benefited from hyperspectral imaging (HSI), which captures spectral signatures linked to biochemical changes during storage.
ViTAMINS is a method that incorporates synthetic hard negatives into unsupervised vision transformer pretraining to enhance representation quality. The approach is evaluated on ImageNet and a range of downstream tasks—including transfer learning, image retrieval, copy detection, and image/video segmentation—showing significant performance gains. The synthetic negatives also lead to emergent properties, such as representations that encode explicit semantic information and act as strong classifiers, improving over baselines by up to 11.3%.
CoAtNet-DeepMoE is a lightweight Convolution‑Attention hybrid architecture that incorporates a DeepSeek Mixture‑of‑Experts to reduce parameters while maintaining high accuracy for tomato disease classification. The model achieves state‑of‑the‑art performance on Kaggle and PlantVillage datasets, reporting 99.80% accuracy on Kaggle and 99.83% accuracy on PlantVillage, all with only 2.47 million parameters. The source code will be released on GitHub.
arXiv:2607. 13043v1 Announce Type: cross Abstract: Deep learning models achieve state-of-the-art image classification but face deployment challenges due to computational costs and energy demands.
arXiv:2608.28161v1 Announce Type: cross Abstract: Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are of...