ON-P LANT SIZE AND WEIGHTE STIMATION OF TOMATO FRUITS USING DEEP NEURAL NETWORKS AND RGB-DIMAGING

. The size and weight of fruits are crucial factors in yield prediction and determining harvesting time. Machine vision, including fruit detection, is a key technology in the automated monitoring and harvesting of fruits. In particular, deep learning-based fruit-detection methods have been actively applied. Estimation of fruit size after fruit detection requires depth information, which can be acquired using depth imaging. RGB-D cameras include color and depth information required for fruit detection and size estimation. In this study, the RGB-D imaging technique was used to estimate the size and weight of tomatoes. Furthermore, deep learning-based instance segmentation models, including Mask R-CNN, YOLACT, and RTMDet for tomato fruit detection, were trained and evaluated. The proposed method estimated the fruit width with a root mean square error (RMSE) of 4 mm, a mean absolute percentage error (MAPE) of 4.28%, and a fruit height with an RMSE of 5.12 mm and a MAPE of 6.42%. Furthermore, the weight-prediction model based on the area index estimated the tomato fruit weight with an RMSE of 19.69 g and MAPE of 9.44%. Thus, the method can be used for accurate size and weight estimation and can be applied in growth monitoring and automated tomatoes harvesting.