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Cited by 120Smart Agriculture and AIAdvanced Neural Network ApplicationsBerry genetics and cultivation research

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Detection of fruit maturity stage and yield estimation in wild blueberry using deep learning convolutional neural networks

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MacEachern, Craig B., Esau, Travis J., Schumann, Arnold W., Hennessy, Patrick J., Zaman, Qamar U.·2023·Smart Agricultural Technology
DOI 10.1016/j.atech.2022.100099
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Abstract

This study looked at the development of six deep learning artificial neural network models for detecting ripeness stage in wild blueberries, along with developing models for yield estimation. The six networks used were YOLOv3, YOLOv3-SPP, YOLOv3-Tiny, YOLOv4, YOLOv4-Small and YOLOv4-Tiny. Both 3-class (green berries, red berries, blue berries) and 2-class (unripe berries, ripe berries) models were developed with YOLOv4 performing the best with mean average precisions of 79.79% and 88.12% respectively. This result was further supported by YOLOv4 achieving the highest F1 score of 0.82. YOLOv4-Tiny performed the best from a computational load perspective having a mean inference time of 7.8 ms and a mean memory usage of 1.63 GB for single 1280 × 736 pixel images. Only minor differences in the accuracy of the nonlinear regression yield prediction models were detected, with YOLOv4-Small performing the best with a mean absolute error of 24.1%. Despite this error, the results are encouraging, and this novel approach to yield estimation in wild blueberries will aid growers in making better, more localized, management decisions, improving yields and ultimately increasing profits by better understanding their fields ripening characteristics.

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Metadata

Authors
MacEachern, C. B., Esau, T. J., Schumann, A. W., Hennessy, P. J., & Zaman, Q. U.
Affiliations
  • Dalhousie University (CA)
  • University of Florida (US)
Corresponding author
Esau, Travis J.
ORCID
Venue
Smart Agricultural Technology
Publisher
Elsevier BV
Published
2023-02
Volume
3
Pages
100099
ISSN
2772-3755 (ISSN-L)
DOI
10.1016/j.atech.2022.100099
OpenAlex
W4288460555
Cited by
120
References
5
Citations by year
2026: 20 · 2025: 43 · 2024: 27 · 2023: 27
FWCI
13.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Smart Agricultural Technology (publishedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Berry genetics and cultivation research
Keywords
Vaccinium angustifolium, fruit ripeness detection, fruit maturity stages, yield estimation, YOLOv4, YOLOv3, object detection, convolutional neural networks, nonlinear regression, computational efficiency

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CoChat. (2026). Citation check: Detection of fruit maturity stage and yield estimation in wild blueberry using deep learning convolutional neural networks. https://app.cochat.ai/papers/10.1016/j.atech.2022.100099
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MacEachern, C. B., Esau, T. J., Schumann, A. W., Hennessy, P. J., & Zaman, Q. U. (2023). Detection of fruit maturity stage and yield estimation in wild blueberry using deep learning convolutional neural networks. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2022.100099