Design an Ant Lion-Based Yolo-V5 Model for Prediction and Classification of Paddy Leaf Disease - Mishra, Nilamadhab; Seetha, J.; Dinesh Kumar, Arra Ganga; Menon M., Supriya; Ravuri, Ananda (International Journal of Intelligent Systems and Applications in Engineering, 2023)
It is crucial to identify crop diseases early to educate farmers on how to stop the spread of diseases in their crops. However, the agriculture sector's output is impacted by the emergence of numerous crop-related diseases. Multiple methods for predicting paddy leaf diseases have been created, but they still suffer from overfitting, poor detection, and classification issues. To overcome these issues, design a novel Ant Lion-based YOLO-V5 (AL-YOLOv5) system to improve the system's functionality to detect paddy leaf disease. Paddy leaf photos were initially gathered from the internet and trained in the system. Brown spot, Leaf blast, Healthy, and Hispa are the four paddy leaf diseases the proposed model intends to classify better and identify. The dataset's noise is removed during the preprocessing stage, and the GrabCut algorithm is used to segment the impacted areas based on the pixels. The Grey-Level Co-Occurrence Matrix (GLCM), which extracts form, texture, and color features, is also used for feature extraction. Finally, utilize a YOLOv5 network to find and categorize the crop's affected diseases. The created model uses ant lion fitness to forecast paddy leaf diseases correctly. By achieving improved performance metrics, the experimental findings demonstrate the effectiveness of the designed model, and the obtained results are validated with other traditional models in terms of accuracy, precision, recall, F-score, and error rate.