Yield estimation in vineyards by visual grape detection

S. Nuske, S. Achar, T. Bates, S. Narasimhan, S. Singh
2011 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems  
The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields would allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse -small samples are taken from the vineyards during the growing season and extrapolated to determine overall yield. We present an automated
more » ... hod that uses computer vision to identify and count grape berries. These counts are used to generate per vine estimates of crop yield. Both shape and visual texture are used to detect berries. We demonstrate detection of green berries against a green leaf background. We present crop yield estimation results, with the actual harvest yield as groundtruth for 200 vines (over 450 meters) of two different grape varieties. We calibrate our berry count to yield and find that we can predict yield to within 9.8% of actual crop weight.
doi:10.1109/iros.2011.6048830 fatcat:bxc6wqr2i5akddjn52uscypi3m