Determination of Optimal Dataset Characteristics for Improving YOLO Performance in Agricultural Object Detection

Recent advances in artificial intelligence and computer vision have led to significant progress in the use of agricultural technologies for yield prediction, pest detection, and real-time monitoring of plant conditions. However, collecting large-scale, high-quality image datasets in the agriculture sector remains challenging, particularly for specialized datasets such as plant disease images. This study analyzed the effects of the image size (320–640+) and the number of labels on the performance of a YOLO-based object detection model using diverse agricultural datasets for strawberries, tomatoes, chilies, and peppers. Model performance was evaluated using the intersection over union and average precision (AP), where the AP curve was smoothed using the Savitzky–Golay filter and EEM. The results revealed that increasing the number of labels improved the model performance to a certain degree, after which the performance gradually diminished. Furthermore, while increasing the image size from 320 to 640 substantially enhanced the model performance, additional increases beyond 640 yielded only marginal improvements. However, the training time and graphics processing unit usage scaled linearly with increasing image sizes, as larger size images require greater computational resources. These findings underscore the importance of an optimal strategy for selecting the image size and label quantity under resource constraints in real-world model development.