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
收藏
页数:13
相关论文
共 38 条
[1]
Bentini M, 2002, T ASAE, V45, P547, DOI 10.13031/2013.8848
[2]
Bochkovskiy A., 2020, PREPRINT, DOI DOI 10.48550/ARXIV.2004.10934
机构:
Cent Elect Engn Res Inst, CSIR, Pilani, Rajasthan, IndiaCent Elect Engn Res Inst, CSIR, Pilani, Rajasthan, India
Dhiraj
Jain, Deepak Kumar
论文数: 0引用数: 0
h-index: 0
机构:
Chongqing Univ Posts & Telecommun, Coll Automat, Key Lab Intelligent Air Ground Coperat Control Un, Chongqing 400065, Peoples R ChinaCent Elect Engn Res Inst, CSIR, Pilani, Rajasthan, India
机构:
Chonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
Fuentes, Alvaro
Yoon, Sook
论文数: 0引用数: 0
h-index: 0
机构:
Mokpo Natl Univ, Res Inst Realist Media & Technol, Jeonnam 534729, South Korea
Mokpo Natl Univ, Dept Comp Engn, Jeonnam 534729, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
Yoon, Sook
Kim, Sang Cheol
论文数: 0引用数: 0
h-index: 0
机构:
Natl Inst Agr Sci, Suwon 441707, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
Kim, Sang Cheol
Park, Dong Sun
论文数: 0引用数: 0
h-index: 0
机构:
Chonbuk Natl Univ, IT Convergence Res Ctr, Jeonju 54896, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
机构:
Cent Elect Engn Res Inst, CSIR, Pilani, Rajasthan, IndiaCent Elect Engn Res Inst, CSIR, Pilani, Rajasthan, India
Dhiraj
Jain, Deepak Kumar
论文数: 0引用数: 0
h-index: 0
机构:
Chongqing Univ Posts & Telecommun, Coll Automat, Key Lab Intelligent Air Ground Coperat Control Un, Chongqing 400065, Peoples R ChinaCent Elect Engn Res Inst, CSIR, Pilani, Rajasthan, India
机构:
Chonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
Fuentes, Alvaro
Yoon, Sook
论文数: 0引用数: 0
h-index: 0
机构:
Mokpo Natl Univ, Res Inst Realist Media & Technol, Jeonnam 534729, South Korea
Mokpo Natl Univ, Dept Comp Engn, Jeonnam 534729, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
Yoon, Sook
Kim, Sang Cheol
论文数: 0引用数: 0
h-index: 0
机构:
Natl Inst Agr Sci, Suwon 441707, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea
Kim, Sang Cheol
Park, Dong Sun
论文数: 0引用数: 0
h-index: 0
机构:
Chonbuk Natl Univ, IT Convergence Res Ctr, Jeonju 54896, South KoreaChonbuk Natl Univ, Dept Elect Engn, Jeonju 54896, South Korea