A Banana Ripeness Detection Model Based on Improved YOLOv9c Multifactor Complex Scenarios

With the advancement of machine vision technology, deep learning and image recognition have become research hotspots in the non-destructive testing of agricultural products. Moreover, using machine vision technology to identify different ripeness stages of fruits is increasingly gaining widespread attention. During the ripening process, bananas undergo significant appearance and nutrient content changes, often leading to damage and food waste. Furthermore, the transportation and sale of bananas are subject to time-related factors that can cause spoilage, necessitating that staff accurately assess the ripeness of bananas to mitigate unwarranted economic losses for farmers and the market. Considering the complexity and diversity of testing environments, the detection model should account for factors such as strong and weak lighting, image symmetry (since there will be symmetrical banana images from different angles in real scenes to ensure model stability), and other factors, while also eliminating noise interference present in the image itself. To address these challenges, we propose methods to improve banana ripeness detection accuracy under complex environmental conditions. Experimental results demonstrate that the improved ESD-YOLOv9 model achieves high accuracy in these conditions.