The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Accurate yield estimates are of great value to vineyard growers to make informed management decisions such as crop thinning, shoot thinning, irrigation and nutrient delivery, preparing for harvest and planning for market. Current methods are labor intensive because they involve destructive hand sampling and are practically too sparse to capture spatial variability in large vineyard blocks. Here we re- port on an approach to predict vineyard yield automatically and non-destructively using images collected from vehicles driving along vineyard rows. Computer vision algorithms are applied to detect grape berries in images that have been registered together to generate high-resolution estimates. We propose an underlying model relating image measurements to harvest yield and study practical approaches to calibrate the two. We report on results on datasets of several hundred vines collected both early and in the middle of the growing season. We find that it is possible to estimate yield to within 4\% using calibration data from prior harvest data and 3\% using calibration data from destructive hand samples at the time of imaging.

Keywords

Agricultural robotics, computer vision, field robotics

Notes

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): Autonomous Vineyard Canopy and Yield Estimation

Text Reference

Stephen T. Nuske, Kamal Gupta, Srinivasa G. Narasimhan, and Sanjiv Singh, "Modeling and Calibration Visual Yield Estimates in Vineyards," International Conference on Field and Service Robotics, July, 2012.

BibTeX Reference

@inproceedings{Nuske_2012_7070,
   author = "Stephen T. Nuske and Kamal Gupta and Srinivasa G Narasimhan and Sanjiv Singh",
   title = "Modeling and Calibration Visual Yield Estimates in Vineyards",
   booktitle = "International Conference on Field and Service Robotics",
   month = "July",
   year = "2012",
}

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