arXiv AI
Jun 16
An Ensemble Deep Learning Approach for Reliable and Scalable Lemon Leaf Disease Classification
arXiv:2606. 14871v1 Announce Type: cross Abstract: Early detection of plant diseases is crucial to plants and for the farmers.
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.
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arXiv AI
Jul 7
PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification
arXiv:2405. 07332v2 Announce Type: cross Abstract: Numerous applications have resulted from the automation of agricultural disease segmentation using deep learning techniques.
arXiv Machine Learning
Jul 31
Improved Classification of Nitrogen Stress Severity in Plants Under Combined Stress Conditions Using Spatio-Temporal Deep Learning Framework
arXiv:2509. 06625v3 Announce Type: replace-cross Abstract: Plants in their natural habitats endure an array of interacting stresses, both biotic and abiotic, that rarely occur in isolation.
arXiv AI
Jun 2
Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift
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 AI
Aug 12
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
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 Machine Learning
Jul 9
Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops
arXiv:2607. 06585v1 Announce Type: cross Abstract: Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually, resulting in economic damages exceeding USD 220 billion.
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).
arXiv Machine Learning
Jul 30
Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models
arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.
arXiv Machine Learning
Aug 4
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction
arXiv:2608. 01202v1 Announce Type: cross Abstract: Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management.
Hugging Face Trending Papers
Aug 2
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction
Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques.
arXiv AI
Aug 12
Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection
arXiv:2608. 09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis.
arXiv AI
Jun 19
Interpretable Sperm Morphology Classification via Attention-Guided Deep Learning
arXiv:2606. 20438v1 Announce Type: new Abstract: Male infertility is a major cause of couple infertility, often linked to abnormal sperm morphology.