Cotton Leaf and Plant Disease Identification using Intelligent Deep Learning Technique - Shrivastava , Abhishek (International Journal of Intelligent Systems and Applications in Engineering, 2023)
India has the second-largest population and a vast range of crops. Most farmers produce cotton because it’s lucrative, but cotton leaf disease has caused widespread crop failure and reduced farmers’ income and quality of life in recent decades. Cercospora, Bacterial blight, Ascochyta blight, and Target spot may affect cotton leaves. Farmers’ broad-brush assessments may be expensive and inaccurate. Cotton Leaf Disease Early diagnosis is difficult for farmers. If crops are infected early, farmers and crops will suffer. Farmers grow disease-free crops. Visual assessments of cotton leaf life are often inaccurate. A deep learning-based technique analyses plant leaf images to detect disease and estimate cotton quality. Uploading a photograph produces a digital, colour image of a damaged leaf. The picture will be processed using the proposed CNN to predict cotton leaf sickness. The technique aims to produce agricultural disease-detecting technology. The user uploads a sick leaf digital colour picture to start image processing. Finally, CNN can forecast illness. Plant disease diagnostics may avoid a pandemic. Fungi, bacteria, and viruses commonly kill plants. Farmers used their sight to spot illness. This study recommends early agricultural disease detection and fast action to reduce crop losses. Cotton productivity plummets due to disease. We’re studying the cotton leafand plant. Alternaria, Cercospora, Red, White, and Yellow Spots on the Leaf cause 90–99% of cotton leaf diseases. The technique is 99.67% effective.