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Automated Crop Yield Estimation for Apple Orchards
Open accessAbstract
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.
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Citations
Cited by
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043
Sensors and systems for fruit detection and localization: A review
Gongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.05.021 · Cited by 572
Deep fruit detection in orchards
Bargoti, S., & Underwood, J. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989417 · Cited by 607
Counting Apples and Oranges With Deep Learning: A Data-Driven Approach
Chen, S. W., Shivakumar, S. S., Dcunha, S., Das, J., Okon, E., Qu, C., Taylor, C. J., & Kumar, V. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2651944 · Cited by 406
Design, integration, and field evaluation of a robotic apple harvester
Silwal, A., Davidson, J. R., Karkee, M., Mo, C., Zhang, Q., & Lewis, K. (2017) · Journal of Field Robotics · doi:10.1002/rob.21715 · Cited by 406
Metadata
- Authors
- Wang, Q., Nuske, S., Bergerman, M., & Singh, S.
- Venue
- Springer Tracts in Advanced Robotics
- Publisher
- Springer International Publishing
- Published
- 2013
- Pages
- 745-758
- ISSN
- 1610-742X (ISSN-L)
- 1610-7438
- OpenAlex
- W2134974756
- Cited by
- 224
- References
- 0
- FWCI
- 22.64
- Type
- book chapter
- Language
- en
- Open access
- green
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- Wang, Q., Nuske, S., Bergerman, M., & Singh, S. (2013). Automated Crop Yield Estimation for Apple Orchards. Springer Tracts in Advanced Robotics. https://doi.org/10.1007/978-3-319-00065-7_50
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