A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8 Article Swipe
Fuqin Deng
,
Zhenghong He
,
Lanhui Fu
,
Jianle Chen
,
Nannan Li
,
Weibiao Chen
,
Jialong Luo
,
Weilai Qiao
,
Jianfeng Hou
,
Yongkang Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2025.1472230
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2025.1472230
Current object detection algorithms lack accuracy in detecting citrus maturity color, and feature extraction needs improvement. In automated harvesting, accurate maturity detection reduces waste caused by incorrect evaluations. To address this issue, this study proposes an improved YOLOv8-based method for detecting Xinhui citrus maturity. GhostConv was introduced to replace the ordinary convolution in the Head of YOLOv8, reducing the number of parameters in the model and enhancing detection accuracy. The CARAFE (Content-Aware Reassembly of Features) upsampling operator was used to replace the conventional upsampling operation, retaining more details through feature reorganization and expansion. Additionally, the MCA (Multidimensional Collaborative Attention) mechanism was introduced to focus on capturing the local feature interactions between feature mapping channels, enabling the model to more accurately extract detailed features, thus further improving the accuracy of citrus color identification. Experimental results show that the precision, recall, and average precision of the improved YOLOv8 on the test set are 88.6%, 93.1%, and 93.4%, respectively. Compared to the original model, the improved YOLOv8 achieved increases of 16.5%, 20.2%, and 14.7%, respectively, and the parameter volume was reduced by 0.57%. This paper aims to improve the model for detecting Xinhui citrus maturity in complex orchards, supporting automated fruit-picking systems.
Related Topics
Concepts
Upsampling
Computer science
Feature (linguistics)
Artificial intelligence
Focus (optics)
Feature extraction
Maturity (psychological)
Pattern recognition (psychology)
Convolution (computer science)
Algorithm
Computer vision
Image (mathematics)
Artificial neural network
Philosophy
Physics
Linguistics
Psychology
Optics
Developmental psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpls.2025.1472230
- OA Status
- gold
- Cited By
- 5
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406935937
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406935937Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fpls.2025.1472230Digital Object Identifier
- Title
-
A new maturity recognition algorithm for Xinhui citrus based on improved YOLOv8Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-01-29Full publication date if available
- Authors
-
Fuqin Deng, Zhenghong He, Lanhui Fu, Jianle Chen, Nannan Li, Weibiao Chen, Jialong Luo, Weilai Qiao, Jianfeng Hou, Yongkang LuList of authors in order
- Landing page
-
https://doi.org/10.3389/fpls.2025.1472230Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fpls.2025.1472230Direct OA link when available
- Concepts
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Upsampling, Computer science, Feature (linguistics), Artificial intelligence, Focus (optics), Feature extraction, Maturity (psychological), Pattern recognition (psychology), Convolution (computer science), Algorithm, Computer vision, Image (mathematics), Artificial neural network, Philosophy, Physics, Linguistics, Psychology, Optics, Developmental psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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