THESIS

Hyperparameter Tuning Automation in YOLO (Case Study: Corn Leaf Disease)

ABSTRACT

Corn cultivation is pivotal in Southeast Asia, significantly contributing to regional food sccurity and cconomics. However, leaf discases posc a threat, leading to substantial losses in production and harvest quality. To tackle this issue, artificial intelligence (AI) technology is leveraged for early detection of corn leaf diseases. One effective approach is the use of YOLO (You Only Look Once) based object detection models. This study aims to automate the hyperparameter tuning process in YOLO models for corn leaf disease detection, focusing on improving model performance Through meticulous evaluation utilizing precision, recall, mAP50, and mAP50-95 metrics, the study identifies YOLOv8m and YOLO-NAS-L as top-performing models YOLOv8m excels in mAP50 (98.5%) and mAP50-95 (67.8%), while YOLO-NAS-L demonstrates superior detection capabilities with mAP50 (70.3%) and mAP50-95 (38.9%). These findings underscore the potential of advanced AI-driven detection Systems in revolutionizing crop management, facilitating early disease identification, and enabling prompt preventive measures. By leveraging sophisticated object detection models, farmers can enhance crop yields, mitigate losses due to plant diseases, and boost agricultural productivity. The research lays a solid foundation for developing integrated, scalable disease detection systems, offering crucial support for global food security and farmer welfare.

PUBLICATION

• Comparison Study of Corn Leaf Disease Detection based on Deep Learning YOLO-v5 and YOLO-v8 (link)
• Performance evaluation of hyper-parameter tuning automation in YOLOV8 and YOLO-NAS for corn leaf disease detection (link)
• Enhancing the Red Wine Quality Classification Using Ensemble Voting Classifiers (link)