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BIOINFOLET - A Quarterly Journal of Life Sciences
Year : 2024, Volume : 21, Issue : 1
First page : ( 62) Last page : ( 66)
Print ISSN : 0973-1431. Online ISSN : 0976-4755.
Article DOI : 10.5958/0976-4755.2024.00022.1

Computer vision system for detection of mango fruit disease

Barhate B. H.*, Barhate Priyanka V.1,**

Bhusawal Arts, Science and P. O. Nahata Commerce College, Bhusawal, Jalgaon

1DNCVPS Shirish M. Chaudhari College, Jalgaon, India

*Email: barhate_1@yahoo.com

**priyankavbarhate@gmail.com

Online published on 26 March, 2024.

Abstract

Fruit diseases are the source of quality losses in agriculture, resulting in economic losses. Computer vision and image processing techniques have been widely employed for detecting and classifying fruit diseases. Present research work proposes a method for automatically classifying Mango fruit images into two categories: diseased and healthy, on the basis of visual attributes. The proposed system consists of five phases Image Acquisition, Preprocessing, Feature extraction, Classification and Performance Evaluation. Color and Texture are the features under consideration for diseased and healthy images of the fruits. Binary classifiers like KNN, SVM and NB were used to classify the images. It was concluded that Support Vector Machine (SVM) had 99.66% accuracy.

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Keywords

Computer Vision, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB).

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