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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Bibliographic Details
Published inJournal of field robotics Vol. 37; no. 2; pp. 263 - 282
Main Authors Häni, Nicolai, Roy, Pravakar, Isler, Volkan
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc 01.03.2020
Subjects
agriculture
Comparative studies
Datasets
Deep learning
Fruits
Identification methods
learning
Machine learning
Mapping
Modular systems
Orchards
perception
Probabilistic models
Semantic segmentation
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Summary: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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ISSN:1556-4959
1556-4967
DOI:10.1002/rob.21902