An Intelligent Hybrid Framework for Brinjal Leaf Disease Detection using Residual VGG-16 and Weighted Fuzzy C-Means Segmentation

Brinjal Leaf disease (Eggplant) identification have become a significant agricultural issue with an alarming rise in recent years, necessitating effective prediction algorithms. In this paper, Residual VGG-16 classifier is proposed for prediction of brinjal leaf disease such as diseased leaf and healthy leaf. Initially, adaptive Gaussian filtering is applied to the brinjal leaf dataset to supress the noise and smoothens out for better image quality. Next, the processed image is given to the Weighted Fuzzy-C-Means clustering,
to calculate the cluster weight value. After that brinjal leaf image is featured using Local Binary Patten (LBP) for analysing local texture structure. Finally, a Residual VGG-16 framework is processed to enhance the classification of brinjal leaf disease for analysis. Using python software proposed framework have accuracy of 95% is accomplished when compared to other techniques.