Chinese Bayberry Detection in an Orchard Environment Based on an Improved YOLOv7-Tiny Model Article Swipe
Zhenlei Chen
,
Mengbo Qian
,
Xiaobin Zhang
,
Jianxi Zhu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture14101725
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture14101725
The precise detection of Chinese bayberry locations using object detection technology is a crucial step to achieve unmanned harvesting of these berries. Because of the small size and easy occlusion of bayberry fruit, the existing detection algorithms have low recognition accuracy for such objects. In order to realize the fast and accurate recognition of bayberry in fruit trees, and then guide the robotic arm to carry out accurate fruit harvesting, this paper proposes a detection algorithm based on an improved YOLOv7-tiny model. The model introduces partial convolution (PConv), a SimAM attention mechanism and SIoU into YOLOv7-tiny, which enables the model to improve the feature extraction capability of the target without adding extra parameters. Experimental results on a self-built Chinese bayberry dataset demonstrate that the improved algorithm achieved a recall rate of 97.6% and a model size of only 9.0 MB. Meanwhile, the precision of the improved model is 88.1%, which is 26%, 2.7%, 4.7%, 6.5%, and 4.7% higher than that of Faster R-CNN, YOLOv3-tiny, YOLOv5-m, YOLOv6-n, and YOLOv7-tiny, respectively. In addition, the proposed model was tested under natural conditions with the five models mentioned above, and the results showed that the proposed model can more effectively reduce the rates of misdetections and omissions in bayberry recognition. Finally, the improved algorithm was deployed on a mobile harvesting robot for field harvesting experiments, and the practicability of the algorithm was further verified.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture14101725
- OA Status
- gold
- Cited By
- 3
- References
- 37
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4403041150Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/agriculture14101725Digital Object Identifier
- Title
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Chinese Bayberry Detection in an Orchard Environment Based on an Improved YOLOv7-Tiny ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-01Full publication date if available
- Authors
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Zhenlei Chen, Mengbo Qian, Xiaobin Zhang, Jianxi ZhuList of authors in order
- Landing page
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https://doi.org/10.3390/agriculture14101725Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/agriculture14101725Direct OA link when available
- Concepts
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Orchard, Computer science, Biology, EcologyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.proposed | 172, 191 |
| abstract_inverted_index.proposes | 72 |
| abstract_inverted_index.unmanned | 17 |
| abstract_inverted_index.YOLOv5-m, | 164 |
| abstract_inverted_index.YOLOv6-n, | 165 |
| abstract_inverted_index.addition, | 170 |
| abstract_inverted_index.algorithm | 75, 125, 209, 226 |
| abstract_inverted_index.attention | 90 |
| abstract_inverted_index.detection | 2, 9, 35, 74 |
| abstract_inverted_index.locations | 6 |
| abstract_inverted_index.mechanism | 91 |
| abstract_inverted_index.mentioned | 183 |
| abstract_inverted_index.occlusion | 29 |
| abstract_inverted_index.omissions | 202 |
| abstract_inverted_index.precision | 142 |
| abstract_inverted_index.verified. | 229 |
| abstract_inverted_index.Meanwhile, | 140 |
| abstract_inverted_index.algorithms | 36 |
| abstract_inverted_index.capability | 105 |
| abstract_inverted_index.conditions | 178 |
| abstract_inverted_index.extraction | 104 |
| abstract_inverted_index.harvesting | 18, 215, 219 |
| abstract_inverted_index.introduces | 84 |
| abstract_inverted_index.self-built | 117 |
| abstract_inverted_index.technology | 10 |
| abstract_inverted_index.YOLOv7-tiny | 80 |
| abstract_inverted_index.convolution | 86 |
| abstract_inverted_index.demonstrate | 121 |
| abstract_inverted_index.effectively | 195 |
| abstract_inverted_index.harvesting, | 69 |
| abstract_inverted_index.parameters. | 112 |
| abstract_inverted_index.recognition | 39, 52 |
| abstract_inverted_index.Experimental | 113 |
| abstract_inverted_index.YOLOv3-tiny, | 163 |
| abstract_inverted_index.YOLOv7-tiny, | 95, 167 |
| abstract_inverted_index.experiments, | 220 |
| abstract_inverted_index.recognition. | 205 |
| abstract_inverted_index.misdetections | 200 |
| abstract_inverted_index.respectively. | 168 |
| abstract_inverted_index.practicability | 223 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.80016842 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |