Review on Plants Leaf Disease Identification Using Deep Learning and Convolutional Neural Network

The application of Convolutional Neural Networks (CNNs) in plant leaf disease detection represents a transformative advancement in agricultural technology. This review article highlights the potential of CNN-based systems to replace traditional manual inspection methods, offering scalable, efficient, and accurate solutions for managing plant health. Traditional disease detection methods are often labor-intensive and prone to error. CNNs provide an alternative by automatically learning from visual data without needing manual feature extraction. The review delves into the architecture of CNNs, emphasizing their ability to extract intricate features from leaf images through convolutional and pooling layers, which enables the identification of subtle patterns associated with plant diseases. The review suggests a move towards integrating various technologies such as CNNs, remote sensing, drones, and AI to create a comprehensive system for plant disease detection. While CNNs have shown great success, there is a need to improve their robustness to handle challenges such as environmental variations, occlusions, and overfitting.