RFA-YOLOv8: A Robust Tea Bud Detection Model with Adaptive Illumination Enhancement for Complex Orchard Environments Article Swipe
Qiuyue Yang
,
Jinan Gu
,
Tao Xiong
,
Qihang Wang
,
Juan Huang
,
Yue Xi
,
Zhongkai Shen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15181982
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture15181982
Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in [email protected] and [email protected]:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, [email protected] and [email protected]:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking.
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RFA-YOLOv8: A Robust Tea Bud Detection Model with Adaptive Illumination Enhancement for Complex Orchard EnvironmentsWork title
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articleOpenAlex work type
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2025Year of publication
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2025-09-19Full publication date if available
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Qiuyue Yang, Jinan Gu, Tao Xiong, Qihang Wang, Juan Huang, Yue Xi, Zhongkai ShenList of authors in order
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https://doi.org/10.3390/agriculture15181982Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/agriculture15181982Direct OA link when available
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1Total citation count in OpenAlex
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| abstract_inverted_index.scenes | 29 |
| abstract_inverted_index.severe | 42 |
| abstract_inverted_index.shoots | 4, 51, 79, 91, 225 |
| abstract_inverted_index.target | 61 |
| abstract_inverted_index.RFAConv | 133 |
| abstract_inverted_index.Second, | 103 |
| abstract_inverted_index.YOLOv8, | 67 |
| abstract_inverted_index.YOLOv8. | 181 |
| abstract_inverted_index.ability | 127 |
| abstract_inverted_index.between | 49 |
| abstract_inverted_index.complex | 28, 227 |
| abstract_inverted_index.created | 97 |
| abstract_inverted_index.crucial | 9 |
| abstract_inverted_index.dataset | 86 |
| abstract_inverted_index.enhance | 71 |
| abstract_inverted_index.factors | 34 |
| abstract_inverted_index.feature | 106 |
| abstract_inverted_index.gardens | 242 |
| abstract_inverted_index.improve | 204 |
| abstract_inverted_index.instead | 142 |
| abstract_inverted_index.limited | 31 |
| [email protected] | 167, 201 |
| abstract_inverted_index.methods | 26 |
| abstract_inverted_index.module, | 122 |
| abstract_inverted_index.module. | 134 |
| abstract_inverted_index.natural | 6, 81 |
| abstract_inverted_index.provide | 215 |
| abstract_inverted_index.remains | 30 |
| abstract_inverted_index.results | 156 |
| abstract_inverted_index.spatial | 125 |
| abstract_inverted_index.strong, | 186 |
| abstract_inverted_index.support | 235 |
| abstract_inverted_index.various | 93 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.Finally, | 135 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.RFCAConv | 116 |
| abstract_inverted_index.SPPFCSPC | 121 |
| abstract_inverted_index.accuracy | 74, 148 |
| abstract_inverted_index.achieves | 162 |
| abstract_inverted_index.bounding | 151 |
| abstract_inverted_index.density, | 41 |
| abstract_inverted_index.employed | 141 |
| abstract_inverted_index.enhanced | 113 |
| abstract_inverted_index.existing | 25 |
| abstract_inverted_index.findings | 211 |
| abstract_inverted_index.function | 139 |
| abstract_inverted_index.improved | 60, 129, 160 |
| abstract_inverted_index.lighting | 94, 190, 228 |
| abstract_inverted_index.optimize | 146 |
| abstract_inverted_index.original | 180 |
| abstract_inverted_index.overlap, | 43 |
| abstract_inverted_index.picking, | 14 |
| abstract_inverted_index.picking. | 245 |
| abstract_inverted_index.proposes | 58 |
| abstract_inverted_index.research | 214 |
| abstract_inverted_index.solution | 218 |
| abstract_inverted_index.training | 100 |
| abstract_inverted_index.yielding | 192 |
| abstract_inverted_index.automated | 18, 244 |
| abstract_inverted_index.detection | 1, 22, 62, 73, 222 |
| abstract_inverted_index.effective | 217 |
| abstract_inverted_index.evaluated | 184 |
| abstract_inverted_index.increases | 173 |
| abstract_inverted_index.lighting, | 200 |
| abstract_inverted_index.moderate, | 187 |
| abstract_inverted_index.optimized | 120 |
| abstract_inverted_index.represent | 172 |
| abstract_inverted_index.technical | 234 |
| abstract_inverted_index.Robustness | 182 |
| abstract_inverted_index.algorithm, | 63 |
| abstract_inverted_index.capability | 108 |
| abstract_inverted_index.conditions | 95 |
| abstract_inverted_index.containing | 87 |
| abstract_inverted_index.extraction | 107 |
| abstract_inverted_index.perception | 126 |
| abstract_inverted_index.robustness | 76 |
| abstract_inverted_index.similarity | 46 |
| abstract_inverted_index.RFA-YOLOv8, | 64 |
| abstract_inverted_index.background. | 54 |
| abstract_inverted_index.conditions, | 191 |
| abstract_inverted_index.demonstrate | 157 |
| abstract_inverted_index.development | 238 |
| abstract_inverted_index.evaluation. | 102 |
| abstract_inverted_index.harvesting. | 19 |
| abstract_inverted_index.integrating | 131 |
| abstract_inverted_index.intelligent | 12 |
| abstract_inverted_index.introducing | 115 |
| abstract_inverted_index.management, | 16 |
| abstract_inverted_index.multi-scale | 105 |
| abstract_inverted_index.performance | 23 |
| abstract_inverted_index.theoretical | 232 |
| abstract_inverted_index.environments | 7, 229 |
| abstract_inverted_index.experimental | 155 |
| abstract_inverted_index.facilitating | 11 |
| abstract_inverted_index.improvements | 193 |
| [email protected]:0.95 | 203 |
| abstract_inverted_index.positioning. | 153 |
| abstract_inverted_index.Consequently, | 55 |
| abstract_inverted_index.environments. | 82 |
| [email protected]:0.95, | 169 |
| abstract_inverted_index.respectively, | 170 |
| abstract_inverted_index.respectively. | 209 |
| abstract_inverted_index.high-precision | 221 |
| abstract_inverted_index.self-constructed | 85 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.91396547 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |