Cargando…

Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN

Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working...

Descripción completa

Detalles Bibliográficos
Autores principales: Guan, Zhuohuai, Li, Haitong, Chen, Xu, Mu, Senlin, Jiang, Tao, Zhang, Min, Wu, Chongyou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735840/
https://www.ncbi.nlm.nih.gov/pubmed/36502249
http://dx.doi.org/10.3390/s22239550
_version_ 1784846871636738048
author Guan, Zhuohuai
Li, Haitong
Chen, Xu
Mu, Senlin
Jiang, Tao
Zhang, Min
Wu, Chongyou
author_facet Guan, Zhuohuai
Li, Haitong
Chen, Xu
Mu, Senlin
Jiang, Tao
Zhang, Min
Wu, Chongyou
author_sort Guan, Zhuohuai
collection PubMed
description Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working parameters are set according to the operator’s experience and not adjusted during the operation, which leads to the harvest quality fluctuating greatly in a complex environment. In this paper, an impurity-detection system, including a grain-sampling device and machine vision system, was developed. Sampling device structure and impurity extraction algorithm were studied to enhance the impurity identification accuracy. To reduce the effect of impurity occlusion on visual recognition, an infusion-type sampling device was designed. The sampling device light source form was determined based on the brightness histogram analysis of a captured image under different light irradiations. The effect of sampling device structures on impurity visualization, grain distribution, and mass flow rate was investigated by the discrete element method (DEM). The impurity recognition algorithm was proposed based on Mask R-CNN, which mainly includes an impurity feature extraction network, an ROI generation network, and a target segmentation network. The test set experiment showed that the precision rate, recall rate, average precision, and comprehensive evaluation indicator of the impurity recognition model were 92.49%, 88.63%, 81.47%, and 90.52%, respectively. The conversion between impurity pixel number and its actual mass was realized according to the pixel density calibration test and impurity rate correction factor. The bench test result showed that the designed system has a good detection accuracy of 91.15~97.26% for the five varieties. The result relative error was in a range of 5.71~11.72% between the impurity-detection system and manual method in field conditions. The impurity-detection system could be applied to tracked rice combine harvesters to provide a reference for the adjustment of operating parameters.
format Online
Article
Text
id pubmed-9735840
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97358402022-12-11 Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN Guan, Zhuohuai Li, Haitong Chen, Xu Mu, Senlin Jiang, Tao Zhang, Min Wu, Chongyou Sensors (Basel) Article Impurity rate is one of the key performance indicators of the rice combine harvester and is also the main basis for parameter regulation. At present, the tracked rice combine harvester impurity rates cannot be monitored in real time. Due to the lack of parameter regulation basis, the harvest working parameters are set according to the operator’s experience and not adjusted during the operation, which leads to the harvest quality fluctuating greatly in a complex environment. In this paper, an impurity-detection system, including a grain-sampling device and machine vision system, was developed. Sampling device structure and impurity extraction algorithm were studied to enhance the impurity identification accuracy. To reduce the effect of impurity occlusion on visual recognition, an infusion-type sampling device was designed. The sampling device light source form was determined based on the brightness histogram analysis of a captured image under different light irradiations. The effect of sampling device structures on impurity visualization, grain distribution, and mass flow rate was investigated by the discrete element method (DEM). The impurity recognition algorithm was proposed based on Mask R-CNN, which mainly includes an impurity feature extraction network, an ROI generation network, and a target segmentation network. The test set experiment showed that the precision rate, recall rate, average precision, and comprehensive evaluation indicator of the impurity recognition model were 92.49%, 88.63%, 81.47%, and 90.52%, respectively. The conversion between impurity pixel number and its actual mass was realized according to the pixel density calibration test and impurity rate correction factor. The bench test result showed that the designed system has a good detection accuracy of 91.15~97.26% for the five varieties. The result relative error was in a range of 5.71~11.72% between the impurity-detection system and manual method in field conditions. The impurity-detection system could be applied to tracked rice combine harvesters to provide a reference for the adjustment of operating parameters. MDPI 2022-12-06 /pmc/articles/PMC9735840/ /pubmed/36502249 http://dx.doi.org/10.3390/s22239550 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guan, Zhuohuai
Li, Haitong
Chen, Xu
Mu, Senlin
Jiang, Tao
Zhang, Min
Wu, Chongyou
Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
title Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
title_full Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
title_fullStr Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
title_full_unstemmed Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
title_short Development of Impurity-Detection System for Tracked Rice Combine Harvester Based on DEM and Mask R-CNN
title_sort development of impurity-detection system for tracked rice combine harvester based on dem and mask r-cnn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735840/
https://www.ncbi.nlm.nih.gov/pubmed/36502249
http://dx.doi.org/10.3390/s22239550
work_keys_str_mv AT guanzhuohuai developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn
AT lihaitong developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn
AT chenxu developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn
AT musenlin developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn
AT jiangtao developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn
AT zhangmin developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn
AT wuchongyou developmentofimpuritydetectionsystemfortrackedricecombineharvesterbasedondemandmaskrcnn