Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment

Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorith...

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Veröffentlicht in:	Foods Jg. 11; H. 15; S. 2197
Hauptverfasser:	Yang, Ke, Peng, Baoliang, Gu, Fengwei, Zhang, Yanhua, Wang, Shenying, Yu, Zhaoyang, Hu, Zhichao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 24.07.2022 MDPI
Schlagworte:	Accuracy Algorithms Artificial neural networks bulbs Computer vision convolutional neural network Cutting equipment Deep learning Efficiency Food processing food research Food safety food safety control Food science Garlic garlic root cutting Harvest Machine learning Neural networks object detection Object recognition Physiological aspects Sensors standard deviation Transfer learning YOLO United Kingdom
ISSN:	2304-8158, 2304-8158
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Abstract	Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.
AbstractList	Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research. Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning-a convolutional neural network-is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning-a convolutional neural network-is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs with varied brightness, surface attachment, and shape, which enabled sufficient learning of the detector. The optimum minibatches and epochs were obtained by comparing the test results of different training parameters. Research shows that IRM-YOLOv2 is superior to the SqueezeNet, ShuffleNet, and YOLOv2 models of classical neural networks, as well as the YOLOv3 and YOLOv4 algorithm models. The confidence score, average accuracy, deviation, standard deviation, detection time, and storage space of IRM-YOLOv2 were 0.98228, 99.2%, 2.819 pixels, 4.153, 0.0356 s, and 24.2 MB, respectively. In addition, this study provides an important reference for the application of the YOLO algorithm in food research.
Audience	Academic
Author	Zhang, Yanhua Peng, Baoliang Gu, Fengwei Yang, Ke Hu, Zhichao Wang, Shenying Yu, Zhaoyang
AuthorAffiliation	3 Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China 2 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; wangshenying@caas.cn 1 Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China; yk666666yk@126.com (K.Y.); pengbaoliang@caas.cn (B.P.); gufengwei@caas.cn (F.G.); zhangyanhua@caas.cn (Y.Z.)
AuthorAffiliation_xml	– name: 1 Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China; yk666666yk@126.com (K.Y.); pengbaoliang@caas.cn (B.P.); gufengwei@caas.cn (F.G.); zhangyanhua@caas.cn (Y.Z.) – name: 2 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; wangshenying@caas.cn – name: 3 Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Author_xml	– sequence: 1 givenname: Ke surname: Yang fullname: Yang, Ke – sequence: 2 givenname: Baoliang surname: Peng fullname: Peng, Baoliang – sequence: 3 givenname: Fengwei surname: Gu fullname: Gu, Fengwei – sequence: 4 givenname: Yanhua surname: Zhang fullname: Zhang, Yanhua – sequence: 5 givenname: Shenying surname: Wang fullname: Wang, Shenying – sequence: 6 givenname: Zhaoyang surname: Yu fullname: Yu, Zhaoyang – sequence: 7 givenname: Zhichao surname: Hu fullname: Hu, Zhichao
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Snippet	Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object...
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SubjectTerms	Accuracy Algorithms Artificial neural networks bulbs Computer vision convolutional neural network Cutting equipment Deep learning Efficiency Food processing food research Food safety food safety control Food science Garlic garlic root cutting Harvest Machine learning Neural networks object detection Object recognition Physiological aspects Sensors standard deviation Transfer learning YOLO
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Title	Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment
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