Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm

被引:0
|
作者
Zhang, Zhen [1 ,2 ,3 ]
Lei, Xiaohui [2 ,3 ]
Huang, Kai [2 ,3 ]
Sun, Yuanhao [2 ,3 ]
Zeng, Jin [2 ,3 ]
Xyu, Tao [2 ,3 ]
Yuan, Quanchun [2 ,3 ]
Qi, Yannan [2 ,3 ]
Herbst, Andreas [4 ]
Lyu, Xiaolan [2 ,3 ]
机构
[1] Jiangsu Univ, Sch Agr Engn, Zhenjiang, Peoples R China
[2] Jiangsu Acad Agr Sci, Inst Agr Facil & Equipment, Nanjing, Peoples R China
[3] Minist Agr & Rural Affairs, Key Lab Modern Hort Equipment, Nanjing, Peoples R China
[4] JKI, Inst Chem Applicat Technol, Braunschweig, Germany
来源
基金
中国国家自然科学基金;
关键词
pear tree inflorescence; long-distance detection; YOLOv7; EMA; SPPCSPCS; Soft-NMS;
D O I
10.3389/fpls.2023.1330141
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future.
引用
下载
收藏
页数:15
相关论文
共 50 条
  • [41] Underwater Target Detection Based on Improved YOLOv7
    Fu, Junshang
    Tian, Ying
    IAENG International Journal of Computer Science, 2024, 51 (04) : 422 - 429
  • [42] Improved YOLOv7 Algorithm for Wood Surface Defect Detection
    Jiang, Xingwang
    Zhao, Xingqiang
    Computer Engineering and Applications, 2024, 60 (07) : 175 - 182
  • [43] An Underwater Target Detection Algorithm Based on Attention Mechanism and Improved YOLOv7
    Ren, Liqiu
    Li, Zhanying
    He, Xueyu
    Kong, Lingyan
    Zhang, Yinghao
    CMC-COMPUTERS MATERIALS & CONTINUA, 2024, 78 (02): : 2829 - 2845
  • [44] Driver fatigue detection based on improved YOLOv7
    Li, Xianguo
    Li, Xueyan
    Shen, Zhenqian
    Qian, Guangmin
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2024, 21 (03)
  • [45] Lightweight strip steel defect detection algorithm based on improved YOLOv7
    Lu, Jianbo
    Yu, MiaoMiao
    Liu, Junyu
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [46] Rigid tank guide fault detection algorithm based on improved YOLOv7
    Fei Du
    Dandan Mo
    Tianbing Ma
    Jiaxin Fang
    Jinxin Shu
    Jitao Long
    Journal of Real-Time Image Processing, 2025, 22 (1)
  • [47] Pavement Defect Detection Algorithm Based on Improved YOLOv7 Complex Background
    Zou, Chunlong
    Huang, Peile
    Wang, Shenghuai
    Wang, Chen
    Wang, Hongxia
    IEEE ACCESS, 2024, 12 : 32870 - 32880
  • [48] Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography
    Bai, Zhen
    Pei, Xinbiao
    Qiao, Zheng
    Wu, Guangxin
    Bai, Yue
    DRONES, 2024, 8 (03)
  • [49] Transparent Component Defect Detection Method Based on Improved YOLOv7 Algorithm
    Xiao, Qixun
    Huang, Jingde
    Huang, Zhangyu
    Li, Chenyu
    Xu, Jie
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2023, 37 (14)
  • [50] A Fruit Detection Algorithm for a Plum Harvesting Robot Based on Improved YOLOv7
    Sumarac, Jovan
    Kljajic, Jelena
    Rodic, Aleksandar
    ADVANCES IN SERVICE AND INDUSTRIAL ROBOTICS, RAAD 2023, 2023, 135 : 442 - 450