Classification of Tomato Harvest Timing Using an AI Camera and Analysis Based on Experimental Results

Abstract

Smart agriculture has the potential to solve labor shortages and improve production efficiency and prices at the time of shipment. Predicting tomato yields during the cultivation period is crucial for planning shipment volumes and costs in advance. We propose technology that utilizes an AI camera to enable producers to predict yields more accurately, and we verify the effectiveness of the developed system through experimental validation. Specifically, an AI-recognition camera was developed, utilizing You Only Look Once (YOLO) to detect individual tomatoes. The detected tomatoes are analyzed for size using point cloud data. Moreover, the AI-recognition camera performs to classify ripeness based on hue. This technology can achieve accurate ripeness classification without being dependent on the brightness of the greenhouse. To evaluate this AI classification camera, the predicted yield obtained from the camera was compared with the actual harvested yield in the field. The analysis showed an error rate of 6.85%, demonstrating sufficient accuracy for practical implementation. By introducing this system, efficient yield prediction can be achieved, leading to reduced labor costs, stable tomato supply, improved quality, and optimized market distribution. As a result, it is expected to contribute to the benefits of both shippers and consumers.