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Automated Crop Yield Estimation for Apple Orchards

Qi Wang, Stephen T. Nuske, Marcel Bergerman and Sanjiv Singh
Conference Paper, Carnegie Mellon University, 13th Internation Symposium on Experimental Robotics (ISER 2012), July, 2012

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Crop yield estimation is an important task in apple orchard management. The current manual sampling-based yield estimation is time-consuming, labor-intensive and inaccurate. To deal with this challenge, we developed a computer vision-based system for automated, rapid and accurate yield estimation. The system uses a two-camera stereo rig for image acquisition. It works at nighttime with controlled artificial lighting to reduce the variance of natural illumination. An autonomous orchard vehicle is used as the support platform for automated data collection. The system scans both sides of each tree row in orchards. A computer vision algorithm detects and registers apples from acquired sequential images, and then generates apple counts as crop yield estimation. We deployed the yield estimation system in Washington state in September, 2011. The results show that the system works well with both red and green apples in the tall-spindle planting system. The crop yield estimation errors are -3.2% for a red apple block with about 480 trees, and 1.2% for a green apple block with about 670 trees.

BibTeX Reference
title = {Automated Crop Yield Estimation for Apple Orchards},
author = {Qi Wang and Stephen T. Nuske and Marcel Bergerman and Sanjiv Singh},
booktitle = {13th Internation Symposium on Experimental Robotics (ISER 2012)},
sponsor = {This work is supported by the National Institute of Food and Agriculture of the U.S. Department of Agriculture.},
school = {Robotics Institute , Carnegie Mellon University},
month = {July},
year = {2012},
address = {Pittsburgh, PA},