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CES-YOLOv8: Strawberry Maturity Detection Based on the Improved YOLOv8

文献类型： 外文期刊

作者： Chen, Yongkuai 1 ; Xu, Haobin 1 ; Chang, Pengyan 1 ; Huang, Yuyan 1 ; Zhong, Fenglin 2 ; Jia, Qi 4 ; Chen, Lingxiao 5 ; Zhong, Huaiqin 3 ; Liu, Shuang 2 ;

作者机构： 1.Fujian Acad Agr Sci, Inst Digital Agr, Fuzhou 350003, Peoples R China

2.Fujian Agr & Forestry Univ, Coll Hort, Fuzhou 350002, Peoples R China

3.Fujian Acad Agr Sci, Crops Res Inst, Fuzhou 350013, Peoples R China

4.Jiuquan Acad Agr Sci, Jiuquan 735099, Peoples R China

5.Fujian Agr Machinery Extens Stn, Fuzhou 350002, Peoples R China

关键词： automatic harvesting robots; CES-YOLOv8; strawberry maturity

期刊名称：AGRONOMY-BASEL （ 影响因子：3.3； 五年影响因子：3.7 ）

ISSN：

年卷期： 2024 年 14 卷 7 期

页码：

收录情况： SCI

摘要： Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information. This characteristic information includes both simple and intuitive features, as well as deeper abstract meanings. These complex features pose significant challenges to robots in determining fruit ripeness. To increase the precision, accuracy, and efficiency of robotic fruit maturity detection methods, a strawberry maturity detection algorithm based on an improved CES-YOLOv8 network structure from YOLOv8 was developed in this study. Initially, to reflect the characteristics of actual planting environments, the study collected image data under various lighting conditions, degrees of occlusion, and angles during the data collection phase. Subsequently, parts of the C2f module in the YOLOv8 model's backbone were replaced with the ConvNeXt V2 module to enhance the capture of features in strawberries of varying ripeness, and the ECA attention mechanism was introduced to further improve feature representation capability. Finally, the angle compensation and distance compensation of the SIoU loss function were employed to enhance the IoU, enabling the rapid localization of the model's prediction boxes. The experimental results show that the improved CES-YOLOv8 model achieves an accuracy, recall rate, mAP50, and F1 score of 88.20%, 89.80%, 92.10%, and 88.99%, respectively, in complex environments, indicating improvements of 4.8%, 2.9%, 2.05%, and 3.88%, respectively, over those of the original YOLOv8 network. This algorithm provides technical support for automated harvesting robots to achieve efficient and precise automated harvesting. Additionally, the algorithm is adaptable and can be extended to other fruit crops.

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