Vision Transformer-based Plant Disease Classification for Precision Agriculture

A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection

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...

Advanced Deep Learning Framework for Accurate Plant Disease Detection and Classification

ICAG-NET: A Hybrid CNN–Transformer for Real-Time Plant Disease Identification

Plants are among the basic resources available on the earth, as agriculture yields many crops. The diseases of plants can affect global agriculture by influencing the production of agricultural products, and the loss of economic value of crop yield may lead to shortages of food supply. Accurate and quick diagnosis of p...

Plant Disease Detecting System Using CNN

The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.

Wheat leaf disease classification using vision transformer based deep learning approach

This paper uses DL models followed by a vision transformer model (ViTM) to extract multilevel features to focus more on the disease-infected area on the plant with enhanced accuracy, and proves that InceptionV3 outperforms the other models.

PhytoFormer: A Robust Plant Disease Recognition for Crop Protection Using Multi-Branch Hybrid Attention Model

Timely and accurate plant disease detection is essential for sustainable agriculture and global food security. However, existing deep learning approaches still face challenges in recognizing diseases across different plant structures and imaging conditions due to variations in scale, appearance, illumination, and backg...