Author(s) :

Seelam Devi Parvathi, Neethu Krishnan, P. Karputha Pandi

Conference Name :

International Conference on Modern Trends in Engineering and Management (ICMTEM-25)

Abstract :

Orange is a citrus fruit which is one of the agricultural crops that have a lot of added value in worldwide. Early disease diagnosis is essential for citrus fruits, as it is for agricultural products, depending on market demands and possible financial losses. In this paper, a XG Boost classification is proposed to quickly recognise orange fruit disease. Firstly, image is smoothening and removes the noise using Gaussian filter to get high quality image using orange disease dataset. Next, the processed image is given to the Fuzzy C Means clustering for partition of data. After that orange fruit image is featured by Gray Level Co occurrence Matrix (GLCM) textured is designed for further analysis. Finally, a XG Boost framework is processed to enhance the classification of orange fruit disease image. Using python software proposed framework have accuracy of 95% is accomplished when compared to other techniques.

No. of Downloads :

1