Cotton Leaf Disease Detection using Convolutional Neural Networks (CNN)

Deep learning is a subset of artificial intelligence. It's a form of artificial intelligence and machine learning that attempts to simulate the way humans pick up specific types of information. The goal of this project is to create a deep learning model based on convolutional neural networks that can distinguish between healthy and diseased leaves. Due to its useful features in learner autonomy and extraction of features, it has drawn a great deal of attention in past years from researchers and industry professionals alike. Images of healthy and rotting leaves are included in the dataset. It is widely used in fields such as computational linguistics, voice processing, image processing, and video processing. It has also become a center for studies on agricultural plant protection, such as the detection of plant diseases and the assessment of pest ranges. This study has also discussed about some of the problems and issues that are currently being faced and need to be addressed. Library packages such as KERAS, MATPLOTLIB, NUMPY, and OPENCV have been utilized here.