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Recognition of Chinese wolfberry images with windy and sandy noises using improved YOLOv8

文献类型： 外文期刊

作者： Pei, Fengque 1 ; Li, Zhi 2 ; Mei, Song 2 ; Song, Zhiyu 2 ; Shi, Zhigang 4 ; Tang, Dunbing 3 ; Wan, Ru 4 ;

作者机构： 1.Hohai Univ, Coll Mech & Elect Engn, Nanjing 210000, Peoples R China

2.Minist Agr & Rural Affairs, Nanjing Inst Agr Mechanizat, Nanjing 210014, Peoples R China

3.Nanjing Univ Aeronaut & Astronaut, Coll Mech & Elect Engn, Nanjing 210016, Peoples R China

4.Ningxia Acad Agr & Forestry Sci, Chinese Wolfberry Sci Inst, Yinchuan 210037, Peoples R China

关键词： image recognition; Chinese wolfberry; windy and sandy noise; YOLOv8; attention module

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.4 ）

ISSN： 1934-6344

年卷期： 2025 年 18 卷 2 期

页码：

收录情况： SCI

摘要： With the nature of the high wind and sand in western China, the Chinese wolfberry recognition shows a strong relationship with the sandy noise and needs a high-accuracy algorithm. To address this issue, this study aimed to develop an algorithm for accurately detecting and recognizing wolfberries. YOLOv8, an algorithm promoted by Ultralytics, supports image classification, object detection, and instance segmentation tasks. To enhance the performance of the original YOLOv8 model, a novel YOLOv8 algorithm incorporating FasterNet, RepBiFPN, and Lightweight Asymmetric Dual-Head was proposed. Firstly, thousands of Chinese wolfberry images were collected from the Ningxia Academy of Agriculture and Forestry Science, China, and random noises were added to simulate the wind and sand conditions typical of spring. Secondly, leveraging the advantages of YOLOv8n, such as its high speed and accuracy, this research innovatively integrated the FasterNet block into the C2f module of YOLOv8 to improve the effective handling of data uncertainty and noise. Additionally, an innovative RepViT+BiFPN, a new detective head, and a Lightweight Asymmetric Dual-Head were introduced to improve the training efficiency of the YOLOv8 network. Finally, to evaluate the effectiveness of improved YOLOv8 for the recognition of wolfberry, the dataset of wolfberry images was divided into a training set, a validation set, and a testing set to assess the performances of different models. Experiment results demonstrate that the YOLOv8-FasterNet+LADH+RepBiFPN model outperforms other models in terms of mAP@0.50-0.95, achieving a 4.5% improvement on the validation set compared to the original YOLOv8n. This research addresses the high-speed and accurate recognition of the Chinese wolfberry under strong winds and sand noise through algorithmic improvements and integration, which can facilitate the automation and intelligence of Chinese wolfberry harvesting and contribute to the advancement of agricultural mechanization.

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