StarNet-Embedded Efficient Network for On-Tree Palm Fruit Ripeness Identification in Complex Environments

Jiehao Li, Tao Zhang, Shan Zeng, Qiaoming Gao, Lianqi Wang and Jiahuan Lu ()
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Jiehao Li: National Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China
Tao Zhang: National Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China
Shan Zeng: National Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China
Qiaoming Gao: Guangxi Hepu County Huilaibao Manufacturing Co., Ltd., Beihai 536100, China
Lianqi Wang: Guangxi Hepu County Huilaibao Manufacturing Co., Ltd., Beihai 536100, China
Jiahuan Lu: National Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 17, 1-17

Abstract: As a globally significant oil crop, precise ripeness identification of palm fruits directly impacts harvesting efficiency and oil quality. However, the progress and application of identifying the ripeness of palm fruits have been impeded by the computational limitations of agricultural hardware and the insufficient robustness in accurately identifying palm fruits in complex on-tree environments. To address these challenges, this paper proposes an efficient recognition network tailored for complex canopy-level palm fruit ripeness assessment. Progressive combination optimization enhances the baseline network, which utilizes the YOLOv8 architecture. This study has individually enhanced the backbone network, neck, detection head, and loss function. Specifically, the backbone integrates the StarNet framework, while the detection head incorporates the lightweight LSCD structure. To enhance recognition precision, StarNet-derived Star Blocks replace standard bottleneck modules in the neck, forming optimized C2F-Star components, complemented by DIoU loss implementation to accelerate convergence. The resultant on-tree model for recognizing palm fruit ripeness achieves substantial efficiency gains. While simultaneously elevating detection precision to 76.0% mAP@0.5, our method’s GFLOPs, parameters, and model size are only 4.5 G, 1.37 M, and 2.85 MB, which are 56.0%, 46.0%, and 48.0% of the original model. The effectiveness of the model in recognizing palm fruit ripeness in complex environments, such as uneven lighting, motion blur, and occlusion, validates its robustness.

Keywords: ripeness identification; palm fruit; image processing; StarNet-embedded; complex agriculture environments (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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