Deep Learning Based Cotton Leaf Disease Detection
Shakti Kinger, Aradhya Tagalpallewar, Reuben Roy George, Kaustubh Hambarde, Piyush Sonawane
Abstract
One of the most widely grown crops in the world, cotton provides a living for many farmers and is essential to the growth of the international economy. However, the actual production of cotton is frequently hampered by a number of problems, with sick cotton leaves ranking highly among them. In order to detect unhealthy cotton leaves, our research uses three distinct deep learning models, including CNN, InceptionV3, and Resnet 152 V2, to categorize cotton leaves or plants as fresh or diseased. The accuracy results for CNN, Inception V3, and Resnet 152V2 are respectively 99.057, 97.170, and 98.113, highlighting the significant contribution these approaches can make to solving this issue.