Improving the Efficiency of Image Recognition for Yuzu Fruit Counting Using Object Recognition Models

Modern agriculture faces a labor shortage due to aging and a decrease in new farmers. Artificial intelligence (AI) and data utilization aim to improve productivity. Crop detection is one example, where object recognition models automate the process compared to manual detection relying on farmer experience. However, the challenge lies in training data requirements and variations in label assignment. This research investigates how different label assignment methods impact object recognition. We compare the labeling conditions using the YOLO and assess their effect on accuracy. Increasing target classes in test data helps maintain precision, while reducing recall. Detailed labeling improves average precision.