Cotton Leaf Disease Detection using AI Techniques: A Comprehensive Survey
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Farming plays an essential role in supporting the economy of many nations. In developing countries particularly, a large number of people depend on agriculture as their main source of income and daily sustenance. Cotton is regarded as one of the most valuable commercial crops because it provides the primary raw material for textile manufacturing industries worldwide. However, cotton cultivation is frequently affected by several leaf diseases that weaken plant growth and reduce both the amount and quality of harvested fiber. When such infections remain unnoticed during early growth stages, they can spread quickly and cause serious losses for farmers. For this reason, identifying cotton leaf diseases at an early stage is extremely important for protecting crop health and maintaining agricultural productivity. In many agricultural settings, farmers determine plant health by visually examining leaves in the field. Although this practice has been used for generations, it often requires considerable effort and time and may not always result in correct diagnosis. Environmental variations and the limited availability of trained agricultural specialists in rural areas can further complicate disease recognition. With the advancement of artificial intelligence and deep learning, researchers are increasingly exploring automated techniques to assist in plant disease identification. This study proposes an intelligent system that analyzes cotton leaf images to detect disease symptoms. A Convolutional Neural Network is used to learn visual characteristics from the images. Image preparation steps such as resizing, normalization, and augmentation improve model learning ability. Such systems can support farmers in recognizing infections earlier, reducing losses and encouraging technology-driven farming
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