TL;DR
A hybrid deep learning model that features the advantages of the ResNet152V2 on local feature representation and Vision Transformer on global contextual representation can be discussed as a powerful and scalable intelligent plant disease detection method and has possible future applications in the areas of precision agriculture and automated systems of crop monitoring.
A deep learning model designed to automatically detect grape leaf diseases based on images, using a pretrained ResNet50 which is trained on ImageNet as feature extractor and a Convolutional Block Attention Module to boost its discriminative capacity is introduced.
According to the historical database there is less quality due to the significant impacts of plant diseases that leads to lack of empowerment for framing as well as growth in agriculture. Early-stage detection is still challenging while identifying symptoms such as minor injuries, discolorations in leaves, major textur...
An integrated hybrid deep learning framework that combines Convolutional Neural Networks, Transformer-based self-attention, and Long Short-Term Memory networks for intelligent cotton leaf disease recognition and can serve as a scalable artificial intelligence solution for precision agriculture applications is introduce...
The occurrence of diseases in rice leaves is a crucial problem for agricultural productivity and food security, requiring the development of accurate algorithms to reliably detect these conditions. Although deep learning methods, especially Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have alrea...
This study applies transfer learning to two CNN architectures, MobileNetV2 and ResNet50, to classify red onion leaf diseases into three classes yellow spot, leaf curl, and healthy leaves, and shows that ResNet50 demonstrates better generalization, with a stable training curve, and outperforms MobileNetV2 in terms of ac...
This study improves potato leaf disease detection using a fine-tuned InceptionV3 with data augmentation and dropout, while Grad-CAM visualizations enhance model interpretability, reliability, and practical utility for accurate agricultural disease diagnosis.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
