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

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 harve...

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Autores principales:	Abozar Nasirahmadi, Ulrike Wilczek, Oliver Hensel
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional neural network damage deep learning harvester sugar beet Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/40693519b1c048b0b2786d0e858e90bd
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spelling	oai:doaj.org-article:40693519b1c048b0b2786d0e858e90bd2021-11-25T15:59:20ZSugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models10.3390/agriculture111111112077-0472https://doaj.org/article/40693519b1c048b0b2786d0e858e90bd2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1111https://doaj.org/toc/2077-0472Mechanical 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.Abozar NasirahmadiUlrike WilczekOliver HenselMDPI AGarticleconvolutional neural networkdamagedeep learningharvestersugar beetAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1111, p 1111 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network damage deep learning harvester sugar beet Agriculture (General) S1-972
spellingShingle	convolutional neural network damage deep learning harvester sugar beet Agriculture (General) S1-972 Abozar Nasirahmadi Ulrike Wilczek Oliver Hensel Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models
description	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.
format	article
author	Abozar Nasirahmadi Ulrike Wilczek Oliver Hensel
author_facet	Abozar Nasirahmadi Ulrike Wilczek Oliver Hensel
author_sort	Abozar Nasirahmadi
title	Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models
title_short	Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models
title_full	Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models
title_fullStr	Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models
title_full_unstemmed	Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models
title_sort	sugar beet damage detection during harvesting using different convolutional neural network models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/40693519b1c048b0b2786d0e858e90bd
work_keys_str_mv	AT abozarnasirahmadi sugarbeetdamagedetectionduringharvestingusingdifferentconvolutionalneuralnetworkmodels AT ulrikewilczek sugarbeetdamagedetectionduringharvestingusingdifferentconvolutionalneuralnetworkmodels AT oliverhensel sugarbeetdamagedetectionduringharvestingusingdifferentconvolutionalneuralnetworkmodels
_version_	1718413375579357184

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

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