End-to-end lightweight berry number prediction for supporting table grape cultivation

•New real-time tech for automatic berry counting aids farmers in thinning.•Novel method to predict berry number from a single 2D image.•Design and implementation of 8 key features from a compact deep learning model.•Achieves low mean absolute error (MAE) of 2.60 berries on extensive dataset. The advent of smart agriculture has revolutionized and streamlined various manual tasks in grape cultivation, one of which is berry thinning. This task necessitates experienced farmers to selectively remove a specific number of berries from the working bunch, as guided by the remaining number of berries in the bunch. In response, this paper introduces a novel real-time edge computing application that automates the process of counting the berries in a working bunch using a single 2D image. The proposed application employs YOLOv5-based object detection techniques (Jocher, 2021) to distinguish each working bunch and the visible and slightly occluded berries contained therein. The key contribution of this paper is to accurately predict the number of berries in the whole bunches including those not visible in a 2D image by harnessing the output from object detection to devise features based solely on bounding box information. In addition, the feature set is optimized by employing a wrapper feature selection method (Kohavi & John, 1997), in consideration of the limitations of edge computing devices. The eight selected features yield a mean absolute error (MAE) of 2.60 berries, tested on a dataset of 26,230 images. Only a slight increase over the initial 19-feature set, which achieved an MAE of 2.42 berries. Furthermore, the proposed approach has been successfully implemented and tested on an Android smartphone, the Sony Xperia 1 III, without the need for an internet connection. The overall computation time per image stands at an average of 0.333 s, confirming its potential for real-world application.