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%.
Bibliographical noteFunding Information:
This study was supported by the USDA NIFA MIN‐98‐G02. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper ( http://www.msi.umn.edu ).
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