A Survey on Deep Learning Techniques in Fruit Disease Detection

Abstract

The improvement in computer vision techniques made the implementation of various agriculture related problems easy. One such problem is fruit disease detection. There has been enormous research on different fruits like the apple, mango, olive, kiwi, orange, passion fruit, and others using deep learning techniques. This article summarizes the major contributions of this field over past few years. As per the authors' knowledge, there is no survey paper specifically on fruit disease detection using deep learning techniques. The technical analysis of deep learning techniques to predict diseases in fruits have been done in this article. The study also presents a comparative study of image acquisition, image pre-processing, and segmentation techniques along with the deep learning models used. The study concluded the fact that the best fit deep learning model can be different depending on the computation power of the system and the data used. Directions of future research have also been discussed in the article.