A Smart Approach to Coconut Leaf Spot Disease Classification using Computer Vision and Deep Learning Technique
Simrat Kaur Brar, Rishabh Sharma, Satvik Vats, Vinay Kukreja
Abstract
Coconut leaf spot (CLS) disease is a major threat to coconut production and can cause severe economic losses. In this study, we propose a deep learning (DL)-based ResNext50 model for automated detection and severity classification of CLS disease. Our model leverages a ResNext50 mode; and is trained and tested on a dataset of coconut leaf images with six severity levels, ranging from healthy leaves to critical severity. The proposed approach achieves high accuracy in detecting and classifying the severity levels of the disease. Our findings suggest that the proposed method is successful in properly identifying and categorizing the illness severity levels, with an accuracy rate of 91.77% overall. The strategy that has been presented has the possibility to significantly improve the efficiency and accuracy of CLS disease detection and monitoring, ultimately leading to better management strategies and increased productivity in the coconut industry.