Review analysis of content based image retrieval for plant disease identification using deep learning algorithms

Abstract

A large segment of revenue in many developing countries comes from agriculture. But the perennial incline of diseases in crops is much severe issues and seeks attention of researchers during the past few years. Agriculturists are unable to make timely prediction of diseases due to lack of knowledge and large cultivation areas. Presently, Machine learning (ML) and its sub branch Deep learning (DL) brings a sea level change in conventional disease prediction methods and incline accuracy also. Using visualization techniques many DL architectures are implemented for identification and classification of plant diseases. This paper provides a systematic review of various existing DL and ML models for content-based image retrieval for data sets related to agriculture and investigate the future scope which helps to learn the potential of DL along with multiple visualization methods for recognizing plant diseases which is fruitful for farmers. Prediction and analysis of diseases instead of leaves at other parts of plants is not done yet, DL can help to work from this perspective also. The main motive of this study is to prediction of diseases at various parts of plant to overcome the issues in agriculture field to incline the profit of Farmers.