Detection of on‐tree chestnut fruits using deep learning and RGB unmanned aerial vehicle imagery for estimation of yield and fruit load

Abstract The on‐tree counting of chestnut fruit (bur) is essential for yield estimation and monitoring of fruit load as well as tree health, which is important in determining the management strategy for orchards, markets, and tree health monitoring. However, the practice is still conducted by manual count or farmers’ intuition. Precise and effective counting method is yet to be established. This study attempted to count chestnut fruits with the object detection algorithm, You Only Look Once (YOLO), using the RGB imagery collected with an unmanned aerial vehicle (UAV). The model trained with 500 images (7866 burs labeled with bounding boxes) in YOLO version 4 (v4) could count the number of burs with a R 2 value of 0.98 and a root mean square error of 6.3 (8.3% of the mean) relative to the manual count in the data set for each whole tree ( n = 53). The R 2 of linear regression between the number of burs obtained with the YOLOv4 model and the total yield was 0.76, with a standard error of 1.08 kg tree −1 (26.4% of coefficient of variation), which was equivalent to the in situ burs count by an expert technician. In addition, the number of burs counted with the YOLOv4 model after the adjustment of trunk circumference was negatively correlated with nut weight. Overall, the study revealed that YOLO algorithm coupled with UAV‐based RGB imagery is a precise and efficient method for on‐tree burs detection, which could be used as an indicator of yield and fruit load.