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Automated Visual Yield Estimation in Vineyards
Author(s) -
Nuske Stephen,
Wilshusen Kyle,
Achar Supreeth,
Yoder Luke,
Narasimhan Srinivasa,
Singh Sanjiv
Publication year - 2014
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21541
Subject(s) - vineyard , yield (engineering) , vine , artificial intelligence , computer vision , wine , computer science , pixel , mathematics , pattern recognition (psychology) , geography , horticulture , materials science , metallurgy , physics , archaeology , optics , biology
We present a vision system that automatically predicts yield in vineyards accurately and with high resolution. Yield estimation traditionally requires tedious hand measurement, which is destructive, sparse in sampling, and inaccurate. Our method is efficient, high‐resolution, and it is the first such system evaluated in realistic experimentation over several years and hundreds of vines spread over several acres of different vineyards. Other existing research is limited to small test sets of 10 vines or less, or just isolated grape clusters, with tightly controlled image acquisition and with artificially induced yield distributions. The system incorporates cameras and illumination mounted on a vehicle driving through the vineyard. We process images by exploiting the three prominent visual cues of texture, color, and shape into a strong classifier that detects berries even when they are of similar color to the vine leaves. We introduce methods to maximize the spatial and the overall accuracy of the yield estimates by optimizing the relationship between image measurements and yield. Our experimentation is conducted over four growing seasons in several wine and table‐grape vineyards. These are the first such results from experimentation that is sufficiently sized for fair evaluation against true yield variation and real‐world imaging conditions from a moving vehicle. Analysis of the results demonstrates yield estimates that capture up to 75% of spatial yield variance and with an average error between 3% and 11% of total yield.