Deep Learning Techniques to Detect Crop Disease and Nutrient Deficiency -A Survey

Agriculture forms the root of the Indian economy on which the industrial and service sectors thrive. Plant diseases cause a significant decrease in the quality and quantity of agricultural products. Early identification of disease symptoms and accurate classification of plant diseases are two critical factors in the agricultural production. Traditional methods which consist of existing image processing techniques and machine learning techniques like SVM, Random Forest algorithms which were primarily used for disease detection and classification are replaced by deep learning techniques due to inefficient detection and inaccurate classification. This paper mainly focuses on classification of plant diseases by different Deep Learning (DL) architectures adopted for tracing and classifying plant diseases with higher accuracy. This study also discusses and lists the merits and demerits of various Deep Learning architectures based on classification accuracy.