Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture

Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different types of stress and diseases. Deep Learning as a solution for disease prevention, detection, and management can increase the yield, reduce the cost and improve the quality of crop. This study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition. The k-fold cross-validation mitigates overfitting by training the model on diverse data subsets, which leads to enhanced generalizability while ensuring reliable performance. The proposed method achieved 99. 60% and 100% as Top₁ and Top₅ accuracy, respectively. The method also achieved a recall of 99. 53%, a precision of 99. 53%, and an F1 score of 99. 60%. During 10 trials, the method consistently performed with an average. Top₁ and Top₅ accuracy of 98. 41% and 100% respectively, recall 98. 53%, precision 98. 39% and F1 score 98. 42%. This study is among the first to apply YOLOv8 classification with 10-fold cross-validation for multi-class cotton leaf disease identification using field-captured images.