Fruit Disease Detection Using Image Processing

Abstract

Nowadays, production of mango fruit decreases because of climatic conditions and environmental concerns like heavy rain, high humidity, reduction in soil nutrients, diversity of associated diseases and disorder problems. Typically, the detection of mango Plant diseases is done by naked eye observation, which provides less accuracy. Low productivity of mango fruit is due to the various diseases affecting mango plants which are not recognized by the farmers as they are illiterate. This paper holds a survey on fruit disease detection using image processing techniques. DIP is a fast and accurate technique for detection of diseases in fruits. Identification and classification of diseases of fruits are done through various algorithms. This paper is fruit disease identification and classification techniques used by different authors. Techniques include clustering and CBS, ANN and different classifiers-based classification of diseases. The main focus of our work is obtaining the analysis of different fruit diseases detection techniques and also provides an overview of these techniques. All the work is done using Python and supporting libraries.