How to Cite: Kanagaraju, P., Aushiq, N. M., & Vanan, R. T. (2022). Disease detection and analysis in fruits using image processing. International Journal of Health Sciences, 6(S8), 1198–1211. https://doi.org/10.53730/ijhs.v6nS8.9879 International Journal of Health Sciences ISSN 2550-6978 E-ISSN 2550-696X © 2022. Manuscript submitted: 9 March 2022, Manuscript revised: 18 May 2022, Accepted for publication: 27 June 2022 1198 Disease detection and analysis in fruits using image processing Dr. P. Kanagaraju Assistant Professor in Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode-637 215, Namakkal District, Tamil Nadu, India Corresponding author email: kanagaraju@ksrct.ac.in N. Mohammed Aushiq Students of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tiruchengode-637 215, Namakkal District, Tamil Nadu, India Email: mohammedaushiq2001@gmail.com R. Tamil Vanan Students of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tiruchengode-637 215, Namakkal District, Tamil Nadu, India Email: tamillimat19@gmail.com Abstract---Fruit diseases are a major problem in the agricultural industry where losses in economic and production are occured. In the existing system, K-means clustering algorithm is used to find whether the fruit is infected or not. Due to low accuracy, it will take more time to show the exact result. In this project, an image processing approach is proposed for identifying apple fruit diseases based on Convolutional Neural Network(CNN). In CNN algorithm, fruit image details are taken by the existing packages in this work. However, it can take a few moments. So, this proposed system can be used to identify fruit diseases quickly and automatically. This proposed approach is composed of the following main steps: getting input image, Image Preprocessing, Identifying affected places, highlighting those affected places, Verifying training set, showing results. Few types of fruit diseases, namely bitter rot, sooty blotch, powdery mildew and fungus images were used for this approach. This approach was tested according to fruit disease type and its stages, such as fresh and affected. The algorithm was used for detecting the disease of the fruit. Images were provided for training, such as fresh apple images, fresh banana images, bitter rot images, sooty blotch images, powdery mildew images and fungus images.. Before the image processing, images were converted to color models, because of finding the most suitable color model for this approach. Local Binary