Emerging Trends in Deep Learning Models for Plant Disease Detection: A Review

Continuous technological evolution leads to automated and dynamic agricultural sector, and reduced human effort for enhanced crop production. Automatic detection of plant disease accurately and efficiently is an important factor to maintain good plant health. Deep learning (DL) models which can ingest and process unstructured data become the leading approach in the early detection and classification of plant diseases. This study presents deep understanding on working of each model and analyzes the accuracy rate and limitations of leaf image based algorithms for plant disease detection. This review focuses on the different emerging algorithms of deep learning models developed over past three years.