VGG-EffAttnNet: Hybrid Deep Learning Model for Automated Chili Plant Disease Classification Using VGG16 and EfficientNetB0 With Attention Mechanism.

Chili plant diseases significantly impact global agriculture, necessitating accurate and rapid classification for effective management. The study introduces VGG-EffAttnNet, a hybrid deep learning model combining VGG16 and EfficientNetB0 with attention mechanisms and Monte Carlo Dropout (MCD) for robust chili plant disease classification. VGG16 captures spatial and hierarchical features, while EfficientNetB0 ensures efficient, high-accuracy learning.

Explainable AI for Cotton Leaf Disease Classification: A Metaheuristic-Optimized Deep Learning Approach.

Cotton leaf diseases significantly impact global cotton yield and quality, threatening the livelihoods of millions of farmers. Traditional diagnostic methods are often slow, subjective, and unsuitable for large-scale agricultural monitoring. This study proposes an interpretable and efficient deep learning (DL) framework for the accurate classification of cotton leaf diseases using a hybrid architecture that combines EfficientNetB3 and InceptionResNetV2.

DBA-DeepLab: Dual-Backbone Attention-Enhanced DeepLab V3+ Model for Plant Disease Segmentation.

Accurate and efficient plant disease segmentation is crucial for early diagnosis and precision agriculture. In this study, we propose a DBA-DeepLab model, i.e.

Enhanced Leaf Disease Segmentation Using U-Net Architecture for Precision Agriculture: A Deep Learning Approach.

This study presents a deep learning-based image segmentation approach for leaf disease identification using the U-Net architecture. Convolutional neural networks (CNNs), particularly U-Net, are effective for precise segmentation tasks and were trained and validated on a high-quality "Leaf Disease Segmentation" dataset. Each image contains annotated regions of unhealthy leaf tissue, enabling the model to distinguish between healthy and infected areas.

Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN-ViT Model.

Maize crop productivity is significantly impacted by various foliar diseases, emphasizing the need for early, accurate, and automated disease detection methods to enable timely intervention and ensure optimal crop management. Traditional classification techniques often fall short in capturing the complex visual patterns inherent in disease-affected leaf imagery, resulting in limited diagnostic performance. To overcome these limitations, this study introduces a robust hybrid deep learning framework that synergistically combines convolutional neural networks (CNNs) and vision transformers (ViTs) for enhanced maize leaf disease classification.