A Study of Fruit Disease Detection using Pattern Classifiers
DOI:
https://doi.org/10.26438/ijcse/v6si3.815Keywords:
ArtificialNeuralNetworks, Supervisedlearning, TextureFeatureExtraction, FruitDiseasesAbstract
A country like India, where economy is strongly driven by agricultural products. If plants are suffering from any kind of disease, it may amount loss in both quantity and quality of the agricultural products. The disease diagnosis is one of the very challenging tasks for farmers. Usually, the disease or the symptoms of the disease such as spots or streaks are seen on the leaves or stem of a plant. Most of the diseases in plants are caused by bacteria, fungi, and viruses. In order to prevent such loss, it is vital to detect and diagnose the disease at the early stage. This paper presents a survey of various fruit disease detections using image processing techniques and neural networks. Various authors have proposed different techniques for fruit disease identification and classification. The techniques such as texture feature extraction using GLCM, color-based segmentation, artificial neural network and different classifiers are used. The focus of work is to carry out the analysis of different fruit disease detection techniques.
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