Survey on Machine Learning Techniques used for Fruit Disease Detection

Abstract

Deep structured learning or hierarchical learning or deep learning in short is part of the family of machine learning methods which are themselves a subset of the broader field of Artificial Intelligence. Each successive layer uses the output from the previous layer as input. Deep neural networks, deep belief networks and recurrent neural networks have been applied to fields such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bioinformatics where they produced results comparable to and in some cases better than human experts have. CNN are applied for deep feature extraction and LSTM is used to detect the class based on extracted features. Deep learning technique that would sort the fruit into abnormal and normal based on the feature such as fruit color, number of fruit spots , and shapes of the fruit, The proposed system achieved an accuracy of 98.17%, The susceptibility of disease detection was less with lesser availability of enhanced detection methods for detecting disease in earlier stages. The issue with various existing algorithms is that the accuracy was reduced so some problems not eliminated. Deep learning delivers methodologies, approaches and functionalities that can help to resolve analytic and predictive analysis accurately.