Bayesian Classifier Based Advanced Fruits Disease Detection

  Rupam Thakur,  Priyanka Mehta

The classical approach for detection and identification of fruit diseases is based on the naked eye observation by experts. In some of the developing countries, consultation with experts is a time consuming and costly affair due to the distant locations of their availability. Automatic detection of fruit diseases is of great significance to automatically detect the symptoms of diseases as early as they appear on the growing fruits. The main goal is to monitor diseases on fruits and suggest better solution for healthy yield and productivity with the help of Artificial Neural Network concept. System uses two image databases, one for training of already stored infected area image and other for execution of query images. For image segmentation, K-Means clustering technique is used. Feature vectors such as image color, morphology, texture and structure of hole are applied for extracting features of each image and for diagnosis of disease morphology gives accurate result. The core area of this work is to increase the automatic detection of fruit disease. The results indicate that proposed method is substantial and it can specifically support a relatively accurate analysis of fruit diseases.

Keywords- Keywords: ANN, Naïve Bayes classification, Fruit diseases, K-means clustering

Unique Identification Number - IJEDR1703180
Page Number(s) - 1237-1241
Pubished in - Volume 5 | Issue 3 | September 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Rupam Thakur,  Priyanka Mehta,   "Bayesian Classifier Based Advanced Fruits Disease Detection", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 3, pp.1237-1241, September 2017, Available at :http://www.ijedr.org/papers/IJEDR1703180.pdf