We present a novel method for yield estimation in apple orchards. Our method takes segmented and registered images of apple clusters as input. It outputs number and location of individual apples in each cluster. Our primary technical contributions are a representation based on a mixture of Gaussians, and a novel selection criterion to choose the number of components in the mixture. The method is experimentally verified on four different datasets using images acquired by a vision platform mounted on an aerial robot, a ground vehicle and a hand-held device. The accuracy of the counting algorithm itself is 91 %. It achieves 81–85% accuracy coupled with segmentation and registration which is significantly higher than existing image based methods.
|Original language||English (US)|
|Title of host publication||Springer Proceedings in Advanced Robotics|
|Publisher||Springer Science and Business Media B.V.|
|Number of pages||10|
|State||Published - 2017|
|Name||Springer Proceedings in Advanced Robotics|
Bibliographical noteFunding Information:
Acknowledgments. This work is supported in part by NSF grant # 1317788, USDA NIFA MIN-98-G02 and the MnDrive initiative.
© 2017, Springer International Publishing AG.
- Apple Orchard
- Expectation Maximization
- Gaussian Mixture Model
- Greedy Method
- Minimum Description Length