Visual yield estimation in vineyards: Experiments with different varietals and calibration procedures

Abstract

A crucial practice for vineyard managers is to control the amount of fruit hanging on their vines to reach yield and quality goals. Current vine manipulation methods to adjust level of fruit are inaccurate and ineffective because they are often not performed according to quantitative yield information. Even when yield predictions are available they are inaccurate and spatially coarse because the traditional measurement practice is to use labor intensive, destructive, hand measurements that are too sparse to adequately measure spatial variation in yield. We present an approach to predict the vineyard yield automatically and non-destructively with cameras. The approach uses camera images of the vines collected from farm vehicles driving along the vineyard rows. Computer vision algorithms are applied to the images to detect and count the grape berries. Shape and texture cues are used to detect berries even when they are of similar color to the vine leaves. Images are automatically registered together and the vehicle position along the row is tracked to generate high resolution yield predictions. Results are presented from four different vineyards, including wine and table-grape varieties. The harvest yield was collected from 948 individual vines, totaling approximately 2.5km of vines, and used to validate the predictions we generate automatically from the camera images. We present different calibration approaches to convert our image berry count to harvest yield and find that we can predict yield of individual vineyard rows to within 10% and overall yield to within 5% of the actual harvest weight.