Plant Disease Identification Using Deep Learning: A Systematic Literature Review

Abstract

This research aims to analyze and summarize recent approaches in plant disease identification and classification using deep learning techniques. Through a systematic literature review, we evaluate the various methodologies, neural network architectures, and datasets used in recent studies in this field. Our findings show that the use of deep learning, especially by utilizing complex neural network architectures, has led to significant improvements in plant disease identification accuracy. One of the key findings is the highest accuracy achieved by the Inception Net CNN architecture-based Deep Learning method in detecting diseases in tomato plants, reaching 99.89%. These results confirm that deep learning approaches have great potential to optimize plant disease management and improve agricultural productivity globally.