A vision-based robust grape berry counting algorithm for fast calibration-free bunch weight estimation in the field

Abstract

Counting the number of berries per bunch is a key component of many yield estimation processes but is exceptionally tedious for farmers to complete. Recent work into image processing in viticulture has produced methods for berry counting, however these require some degree of manual intervention or need calibration to manual counts for different bunch architectures.Therefore, this paper introduces a fast and robust calibration-free algorithm for berry counting for winegrapes to aid yield estimation. The algorithm was tested on 529 images collected in the field at multiple vineyards at different maturity stages and achieved an accuracy of approximately 89% per bunch. As it would mostly likely be used to obtain an average value across a block, the low bias of this method resulted in an average accuracy of 99% and was shown to be robust from pea-sized to harvest and between both red and green bunches.Taking only 0.1 to 1 s per image to process and requiring only a smartphone and small backing board to capture, the algorithm can readily be applied to images which are captured in the field by farmers. This allowed bunch weights to be estimated to within 92% accuracy and assisted larger scale yield estimation processes to achieve accuracies of between 3% and 16%. The robustness of the method lays the foundation for fast fruit-set ratio determination and more detailed bunch architecture studies in vivo on a large scale.