Development of a Support System for Judging the Appropriate Timing for Grape Harvesting

The color of grape bunches is a significant factor when harvesting grapes at the appropriate timing. Judging the suitable color for shipment requires experience and varies from one person to another. We herein describe a support system for grape harvesting based on color estimation. To estimate the color of a bunch of grapes, bunch detection, grain detection, removal of diseased grains, and color estimation should be performed. Models based on deep learning are employed for this series of processes. Since color is strongly affected by sunlight, we propose a multitask model that considers sunlight exposure to achieve a robust color estimation model that exhibits decreased sensitivity to sunlight. Our results show that the color estimation accuracy of the model is 76% when sunlight exposure is not considered and 81% when sunlight exposure is considered. In addition, we performed a practical field test of the developed harvest support system in an actual grape field. The results show that our support system can determine the appropriateness of grape harvest with an accuracy of 90%, demonstrating the effectiveness of the system.