Chili Quality Estimation Using YOLOv8: A Deep Learning Approach

The quality estimation of chili plays a crucial role in the spice market industry. This research paper introduces an innovative method for chili quality estimation using the YOLOv8 architecture, a cutting-edge object detection model. By leveraging deep learning techniques, the proposed method offers an automated and efficient solution to accurately assess the quality of chili. Traditional methods of quality estimation heavily rely on subjective assessment or time-consuming manual processes. In contrast, the proposed approach utilizes the YOLOv8 model, which combines advanced convolutional neural networks and object detection algorithms to achieve precise and fast chili quality estimation. To train the YOLOv8 model, a comprehensive dataset of annotated chili images has been curated, encompassing various quality attributes such as “Broken Chili”, “Damaged Chili”, “Good Chili”, “Loose Seed”, and “Stalk”. Through extensive experiments and fine-tuning, the model’s performance has been optimized to achieve high accuracy in chili quality estimation. The experimental findings illustrate the effectiveness of our approach, with the YOLOv8 model achieving a mean Average Precision (mAP) of 75% for chili quality classification. These results indicate the robustness and reliability of the proposed methodology in accurately assessing chili quality attributes.