Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

被引:16
作者
Nasirahmadi, Abozar [1 ]
Wilczek, Ulrike [1 ]
Hensel, Oliver [1 ]
机构
[1] Univ Kassel, Dept Agr & Biosyst Engn, D-37213 Witzenhausen, Germany
来源
AGRICULTURE-BASEL | 2021年 / 11卷 / 11期
关键词
convolutional neural network; damage; deep learning; harvester; sugar beet; ALGORITHM;
D O I
10.3390/agriculture11111111
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.
引用
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页数:13
相关论文
共 38 条
  • [31] Schwich S., 2020, P 2020 ASABE ANN INT
  • [32] Tzutalin, 2015, Labelimg git code
  • [33] Potato Surface Defect Detection Based on Deep Transfer Learning
    Wang, Chenglong
    Xiao, Zhifeng
    [J]. AGRICULTURE-BASEL, 2021, 11 (09):
  • [34] Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments
    Wu, Dihua
    Lv, Shuaichao
    Jiang, Mei
    Song, Huaibo
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 178
  • [35] Xin Y., 2021, P 2021 ASABE ANN INT
  • [36] A Micro-Damage Detection Method of Litchi Fruit Using Hyperspectral Imaging Technology
    Xiong, Juntao
    Lin, Rui
    Bu, Rongbin
    Liu, Zhen
    Yang, Zhengang
    Yu, Lianyi
    [J]. SENSORS, 2018, 18 (03):
  • [37] Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks
    Ye, Xinyue
    Duan, Lian
    Peng, Qiong
    [J]. SMART CITIES, 2021, 4 (01): : 204 - 216
  • [38] Defect Classification of Green Plums Based on Deep Learning
    Zhou, Haiyan
    Zhuang, Zilong
    Liu, Ying
    Liu, Yang
    Zhang, Xiao
    [J]. SENSORS, 2020, 20 (23) : 1 - 15