Ehancing cotton leaf disease detection and classification through machine learning and deep learning techniques

Abstract

In Bangladesh, cotton has the potential to be a significant revenue crop. To accommodaterising demand, we import 3 billion dollars of cotton yearly (The Business Standard). There isn't an alternative to this issue but to grow cotton. However, the most prevalent issue among farmers was diagnosing crop illness by applying the antiquated growidea. They are unable to identify crop diseases early enough to treat the crops withtheappropriate measures. Particularly in rural areas where farmers suffer fromimproper knowledge leading to crop disease identification. The study demonstrates manyalgorithms and deep learning methods for cotton leaf disease detection. I utilize anMLframework that includes three deep-learning models in it.Model accuracy are compared, and the results show that different architectures perform differently on the task. Compared to the other models, CNN's accuracy is somewhat lower at 82.35%. Theaccuracy of VGG16 is greatly improved to 97.69%, demonstrating its usefulness inthis situation. ResNet50 does well too, with 92.51% accuracy. With the highest accuracyof 99.28%, XceptionV3 beats all other models, proving its exceptional ability to completethis assignment. InceptionV3, which has an accuracy of 94.42%, likewise performs admirably. In conclusion, XceptionV3 has the best accuracy at 99.28%, while CNNhas the lowest accuracy at 82.35%.