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Apple inflorescence recognition of phenology stage in complex background based on improved YOLOv7

文献类型： 外文期刊

作者： Chen, Jincheng 1 ; Ma, Benxue 1 ; Ji, Chao 2 ; Zhang, Jing 2 ; Feng, Qingchun 4 ; Liu, Xin 1 ; Li, Yujie 1 ;

作者机构： 1.Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Peoples R China

2.Xinjiang Acad Agr & Reclamat Sci, Mech Equipment Res Inst, Shihezi 832000, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Northwest Agr Equipment, Shihezi 832003, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

5.Minist Educ Prod Mechanizat Oasis Characterist Eco, Engn Res Ctr, Shihezi 832003, Peoples R China

关键词： Phenology; Apple inflorescence; Deep learning; YOLOv7; Target recognition

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 211 卷

页码：

收录情况： SCI

摘要： Accurate discrimination of apple inflorescence morphology and phenology spatial information distribution of orchard are beneficial to guide chemical spraying of target variables and individual thinning operations of machines. In this study, we propose a recognition method based on an improved YOLOv7 model for detecting apple inflorescence at the bud, initial flowering, and full-bloom flowering stages. To reduce parameters, the Efficient Layer Aggregation Network (ELAN) in YOLOv7 was replaced by a residual network structure containing three convolutional layers. A Squeeze and Excitation Network (SENet) and a Coordinate Attention (CA) were embedded in the last layer of the backbone network and the head network, respectively, to improve the recognition accuracy and sensitivity of apple inflorescence. To more accurately compute the distance between the prediction box and the ground truth box. SIoU bounding box regression loss function was used to refine the regression inference bias and improve the bounding box prediction accuracy. In the detection head network, an 80 x 80 detection head was added to improve the recognition ability of small-scale apple inflorescence. Finally, a phenology apple inflorescence dataset was established for the experiment. The ablation experiment results showed that a proper trick could bring an additional performance improvement to the model. The proposed model outperformed three models proposed in the previous study (YOLOv5s, improved YOLOv5, and YOLOv7). It obtained the best performance with a mAP of 80.1% and a recognition speed of 42.58 frames per second (fps). The practicability and robustness of the recognition method were verified by developing the phenology apple inflorescence detection and recognition system. This finding can provide a theoretical basis and strategy for developing real-time recognition equipment for apple inflorescence during phenology.

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