Advanced Deep Learning Framework for Accurate Plant Disease Detection and Classification

Efficient Tomato Leaf Disease Classification Using CNN

: Tomatoes are among the most important horticultural crops for the processing industry worldwide and play a key role in human diets due to their high nutritional and culinary value. However, plant diseases have become a major threat to global food security, increasing the need for reliable and early detection methods....

Deep Learning for Plant Disease Detection: A Systematic Review

It is suggested that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability.

Vision Transformer-based Plant Disease Classification for Precision Agriculture

Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring

This chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.

Rice Leaf Disease Detection Using a Hybrid Deep Learning Architecture

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...

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.