A comparative study of fruit detection and counting methods for yield mapping in apple orchards
We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation‐based approach for fruit detection and counting and perform extensive comparat...
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| Published in | Journal of field robotics Vol. 37; no. 2; pp. 263 - 282 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc
01.03.2020
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| Subjects | |
| Online Access | Get full text |
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| Abstract | We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation‐based approach for fruit detection and counting and perform extensive comparative analysis against other state‐of‐the‐art techniques. This is the first work comparing multiple fruit detection and counting methods head‐to‐head on the same data sets. Fruit detection results indicate that the semisupervised method, based on Gaussian Mixture Models, outperforms the deep learning‐based methods in the majority of the data sets. For fruit counting though, the deep learning‐based approach performs better for all of the data sets. Combining these two methods, we achieve yield estimation accuracies ranging from 95.56% to 97.83%. |
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| AbstractList | We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation‐based approach for fruit detection and counting and perform extensive comparative analysis against other state‐of‐the‐art techniques. This is the first work comparing multiple fruit detection and counting methods head‐to‐head on the same data sets. Fruit detection results indicate that the semisupervised method, based on Gaussian Mixture Models, outperforms the deep learning‐based methods in the majority of the data sets. For fruit counting though, the deep learning‐based approach performs better for all of the data sets. Combining these two methods, we achieve yield estimation accuracies ranging from 95.56% to 97.83%. |
| Author | Roy, Pravakar Häni, Nicolai Isler, Volkan |
| Author_xml | – sequence: 1 givenname: Nicolai orcidid: 0000-0003-4042-3318 surname: Häni fullname: Häni, Nicolai email: haeni001@umn.edu organization: University of Minnesota – sequence: 2 givenname: Pravakar orcidid: 0000-0002-5410-1486 surname: Roy fullname: Roy, Pravakar organization: University of Minnesota – sequence: 3 givenname: Volkan orcidid: 0000-0002-0868-5441 surname: Isler fullname: Isler, Volkan organization: University of Minnesota |
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| Snippet | We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best... |
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| SubjectTerms | agriculture Comparative studies Datasets Deep learning Fruits Identification methods learning Machine learning Mapping Modular systems Orchards perception Probabilistic models Semantic segmentation |
| Title | A comparative study of fruit detection and counting methods for yield mapping in apple orchards |
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