Detection of cotton leaf curl disease's susceptibility scale level based on deep learning

Cotton, a crucial cash crop in Pakistan, faces persistent threats from diseases, notably the Cotton Leaf Curl Virus (CLCuV). Detecting these diseases accurately and early is vital for effective management. This paper offers a comprehensive account of the process involved in collecting, preprocessing, and analyzing an extensive dataset of cotton leaf images. The primary aim of this dataset is to support automated disease detection systems. We delve into the data collection procedure, distribution of the dataset, preprocessing stages, feature extraction methods, and potential applications. Furthermore, we present the preliminary findings of our analyses and emphasize the significance of such datasets in advancing agricultural technology. The impact of these factors on plant growth is significant, but the intrusion of plant diseases, such as Cotton Leaf Curl Disease (CLCuD) caused by the Cotton Leaf Curl Gemini Virus (CLCuV), poses a substantial threat to cotton yield. Identifying CLCuD promptly, especially in areas lacking critical infrastructure, remains a formidable challenge. Despite the substantial research dedicated to cotton leaf diseases in agriculture, deep learning technology continues to play a vital role across various sectors. In this study, we harness the power of two deep learning models, specifically the Convolutional Neural Network (CNN). We evaluate these models using two distinct datasets: one from the publicly available Kaggle dataset and the other from our proprietary collection, encompassing a total of 1349 images capturing both healthy and disease-affected cotton leaves. Our meticulously curated dataset is categorized into five groups: Healthy, Fully Susceptible, Partially Susceptible, Fully Resistant, and Partially Resistant. Agricultural experts annotated our dataset based on their expertise in identifying abnormal growth patterns and appearances. Data augmentation enhances the precision of model performance, with deep features extracted to support both training and testing efforts. Notably, the CNN model outperforms other models, achieving an impressive accuracy rate of 99% when tested against our proprietary dataset.