Cotton Leaf Disease Classification

The cotton industry is a significant agricultural sector that has a profound impact on the nation's economy. To gauge a nation's economic performance, it is crucial to examine both the quality and quantity of its agricultural production. Early diagnosis of leaf diseases may lead to higher revenues in manufacturing. Various image-processing techniques have been developed throughout the years to identify illnesses that damage leaves. Advancements in technology are accelerating the process, although it is still in its initial phases. The agriculture business has a major challenge in dealing with the rise of leaf diseases. Many diseases, such as powdery mildew, army worm, bacterial blight, target spot, and aphids, may affect cotton plants. Extensive observations may be time-consum-ing, expensive, and sometimes inaccurate for the producers involved. We suggest utilizing machine learning techniques like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression, and Convolutional Neural Network (CNN) to automatically detect diseases on cotton leaves. This method is designed for the agricultural sector to distinguish between healthy and sick leaves. The study found that CNN performs best in image classification as it has the greatest accuracy percentage of 99.1%.