Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone
In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a sm...
Gespeichert in:
| Veröffentlicht in: | International journal of agricultural and biological engineering 2022-07, Vol.15 (4), p.154-162 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 162 |
|---|---|
| container_issue | 4 |
| container_start_page | 154 |
| container_title | International journal of agricultural and biological engineering |
| container_volume | 15 |
| creator | Yang, Lili Tian, Weize Zhai, Weixin Wang, Xinxin Chen, Zhibo Wen, Long Xu, Yuanyuan Wu, Caicong |
| description | In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. First, three driving modes were developed for maize sowing scenarios: manual driving assisted driving and unmanned driving. While sowing, smartphone software and CAN (Controller Area Network) storage devices collected both positional data and engine operating conditions. Second, the tractor trajectory points were divided into kinematic sequences, with six driving cycle indicators built in each series based on the time window. Based on the semantic information of the kinematic sequences, the three operations of sowing, seeds filling, and turning round were well recognized. Last, a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm, regarding three factors: driving cycles, operating behaviors, and driving patterns. When compared to the traditional K-means algorithm, the results demonstrated that the harmonic mean of the precision and recall (F1 score) of sowing behavior recognition, seeds filling behavior recognition, and turning behavior recognition were enhanced by 2.06%, 8.99%, and 21.79%, respectively. In terms of the impacts of driving modes and operating behaviors on fuel consumption, assisted driving mode had the lowest fuel usage for both sowing and turning behavior. Therefore, assisted driving is the most fuel-efficient mode for maize sowing. Combining the three driving modes, the relative error of the fuel consumption prediction model was 0.11 L/h, with the manual driving mode having the lowest relative error at 0.09 L/h. This research method lays the foundation for the optimization of tractor operation behavior, the selection of tractor driving mode, and the fine management of tractor fuel consumption. |
| doi_str_mv | 10.25165/j.ijabe.20221504.7454 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2716599882</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2716599882</sourcerecordid><originalsourceid>FETCH-LOGICAL-c230t-f0fe3f3636383e7e5d8bedb16819d85ded12f171a9696442711508348864cbfb3</originalsourceid><addsrcrecordid>eNo9UMtOwzAQtBBIlMIvoEicE_yOc4SKl1SJC5wtJ163ido42AmIv8dNAe1hR7Ozr0HomuCCCiLFbVe0namhoJhSIjAvSi74CVqQivFcMkFP_zHn5-gixg5jyRUTC1Tfw9Z8tj5kARq_6dux9X1mepu5CXZZ4_s47YeZHALYtpmhd9kYTDOmtui_2n6T-QGCOdRiNsUDEfcmjMPW93CJzpzZRbj6zUv0_vjwtnrO169PL6u7dd5QhsfcYQfMMZlCMShBWFWDrYlUpLJKWLCEOlISU8lKck5Lkn5VjCsleVO7mi3RzXHuEPzHBHHUnZ9Cn1bqJJaiqpSiSSWPqib4GAM4PYQ23fqtCdazn7rTs5_6z0998JP9AJYpbGQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2716599882</pqid></control><display><type>article</type><title>Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Yang, Lili ; Tian, Weize ; Zhai, Weixin ; Wang, Xinxin ; Chen, Zhibo ; Wen, Long ; Xu, Yuanyuan ; Wu, Caicong</creator><creatorcontrib>Yang, Lili ; Tian, Weize ; Zhai, Weixin ; Wang, Xinxin ; Chen, Zhibo ; Wen, Long ; Xu, Yuanyuan ; Wu, Caicong ; 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China ; 2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China</creatorcontrib><description>In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. First, three driving modes were developed for maize sowing scenarios: manual driving assisted driving and unmanned driving. While sowing, smartphone software and CAN (Controller Area Network) storage devices collected both positional data and engine operating conditions. Second, the tractor trajectory points were divided into kinematic sequences, with six driving cycle indicators built in each series based on the time window. Based on the semantic information of the kinematic sequences, the three operations of sowing, seeds filling, and turning round were well recognized. Last, a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm, regarding three factors: driving cycles, operating behaviors, and driving patterns. When compared to the traditional K-means algorithm, the results demonstrated that the harmonic mean of the precision and recall (F1 score) of sowing behavior recognition, seeds filling behavior recognition, and turning behavior recognition were enhanced by 2.06%, 8.99%, and 21.79%, respectively. In terms of the impacts of driving modes and operating behaviors on fuel consumption, assisted driving mode had the lowest fuel usage for both sowing and turning behavior. Therefore, assisted driving is the most fuel-efficient mode for maize sowing. Combining the three driving modes, the relative error of the fuel consumption prediction model was 0.11 L/h, with the manual driving mode having the lowest relative error at 0.09 L/h. This research method lays the foundation for the optimization of tractor operation behavior, the selection of tractor driving mode, and the fine management of tractor fuel consumption.</description><identifier>ISSN: 1934-6344</identifier><identifier>EISSN: 1934-6352</identifier><identifier>DOI: 10.25165/j.ijabe.20221504.7454</identifier><language>eng</language><publisher>Beijing: International Journal of Agricultural and Biological Engineering (IJABE)</publisher><subject>Agricultural equipment ; Algorithms ; Behavior ; Cellular telephones ; Controller area network ; Corn ; Data collection ; Electronic devices ; Farm machinery ; Fuel consumption ; Kinematics ; Neural networks ; Optimization ; Prediction models ; Recognition ; Seeds ; Sensors ; Sequences ; Smartphones ; Support vector machines ; Tractors ; Turning behavior ; Vehicles</subject><ispartof>International journal of agricultural and biological engineering, 2022-07, Vol.15 (4), p.154-162</ispartof><rights>2022. This work is published under https://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Yang, Lili</creatorcontrib><creatorcontrib>Tian, Weize</creatorcontrib><creatorcontrib>Zhai, Weixin</creatorcontrib><creatorcontrib>Wang, Xinxin</creatorcontrib><creatorcontrib>Chen, Zhibo</creatorcontrib><creatorcontrib>Wen, Long</creatorcontrib><creatorcontrib>Xu, Yuanyuan</creatorcontrib><creatorcontrib>Wu, Caicong</creatorcontrib><creatorcontrib>1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China</creatorcontrib><creatorcontrib>2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China</creatorcontrib><title>Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone</title><title>International journal of agricultural and biological engineering</title><description>In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. First, three driving modes were developed for maize sowing scenarios: manual driving assisted driving and unmanned driving. While sowing, smartphone software and CAN (Controller Area Network) storage devices collected both positional data and engine operating conditions. Second, the tractor trajectory points were divided into kinematic sequences, with six driving cycle indicators built in each series based on the time window. Based on the semantic information of the kinematic sequences, the three operations of sowing, seeds filling, and turning round were well recognized. Last, a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm, regarding three factors: driving cycles, operating behaviors, and driving patterns. When compared to the traditional K-means algorithm, the results demonstrated that the harmonic mean of the precision and recall (F1 score) of sowing behavior recognition, seeds filling behavior recognition, and turning behavior recognition were enhanced by 2.06%, 8.99%, and 21.79%, respectively. In terms of the impacts of driving modes and operating behaviors on fuel consumption, assisted driving mode had the lowest fuel usage for both sowing and turning behavior. Therefore, assisted driving is the most fuel-efficient mode for maize sowing. Combining the three driving modes, the relative error of the fuel consumption prediction model was 0.11 L/h, with the manual driving mode having the lowest relative error at 0.09 L/h. This research method lays the foundation for the optimization of tractor operation behavior, the selection of tractor driving mode, and the fine management of tractor fuel consumption.</description><subject>Agricultural equipment</subject><subject>Algorithms</subject><subject>Behavior</subject><subject>Cellular telephones</subject><subject>Controller area network</subject><subject>Corn</subject><subject>Data collection</subject><subject>Electronic devices</subject><subject>Farm machinery</subject><subject>Fuel consumption</subject><subject>Kinematics</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Prediction models</subject><subject>Recognition</subject><subject>Seeds</subject><subject>Sensors</subject><subject>Sequences</subject><subject>Smartphones</subject><subject>Support vector machines</subject><subject>Tractors</subject><subject>Turning behavior</subject><subject>Vehicles</subject><issn>1934-6344</issn><issn>1934-6352</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNo9UMtOwzAQtBBIlMIvoEicE_yOc4SKl1SJC5wtJ163ido42AmIv8dNAe1hR7Ozr0HomuCCCiLFbVe0namhoJhSIjAvSi74CVqQivFcMkFP_zHn5-gixg5jyRUTC1Tfw9Z8tj5kARq_6dux9X1mepu5CXZZ4_s47YeZHALYtpmhd9kYTDOmtui_2n6T-QGCOdRiNsUDEfcmjMPW93CJzpzZRbj6zUv0_vjwtnrO169PL6u7dd5QhsfcYQfMMZlCMShBWFWDrYlUpLJKWLCEOlISU8lKck5Lkn5VjCsleVO7mi3RzXHuEPzHBHHUnZ9Cn1bqJJaiqpSiSSWPqib4GAM4PYQ23fqtCdazn7rTs5_6z0998JP9AJYpbGQ</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Yang, Lili</creator><creator>Tian, Weize</creator><creator>Zhai, Weixin</creator><creator>Wang, Xinxin</creator><creator>Chen, Zhibo</creator><creator>Wen, Long</creator><creator>Xu, Yuanyuan</creator><creator>Wu, Caicong</creator><general>International Journal of Agricultural and Biological Engineering (IJABE)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BVBZV</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>P64</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PIMPY</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>RC3</scope><scope>SOI</scope></search><sort><creationdate>20220701</creationdate><title>Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone</title><author>Yang, Lili ; Tian, Weize ; Zhai, Weixin ; Wang, Xinxin ; Chen, Zhibo ; Wen, Long ; Xu, Yuanyuan ; Wu, Caicong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c230t-f0fe3f3636383e7e5d8bedb16819d85ded12f171a9696442711508348864cbfb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agricultural equipment</topic><topic>Algorithms</topic><topic>Behavior</topic><topic>Cellular telephones</topic><topic>Controller area network</topic><topic>Corn</topic><topic>Data collection</topic><topic>Electronic devices</topic><topic>Farm machinery</topic><topic>Fuel consumption</topic><topic>Kinematics</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Prediction models</topic><topic>Recognition</topic><topic>Seeds</topic><topic>Sensors</topic><topic>Sequences</topic><topic>Smartphones</topic><topic>Support vector machines</topic><topic>Tractors</topic><topic>Turning behavior</topic><topic>Vehicles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Lili</creatorcontrib><creatorcontrib>Tian, Weize</creatorcontrib><creatorcontrib>Zhai, Weixin</creatorcontrib><creatorcontrib>Wang, Xinxin</creatorcontrib><creatorcontrib>Chen, Zhibo</creatorcontrib><creatorcontrib>Wen, Long</creatorcontrib><creatorcontrib>Xu, Yuanyuan</creatorcontrib><creatorcontrib>Wu, Caicong</creatorcontrib><creatorcontrib>1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China</creatorcontrib><creatorcontrib>2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>East & South Asia Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Genetics Abstracts</collection><collection>Environment Abstracts</collection><jtitle>International journal of agricultural and biological engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Lili</au><au>Tian, Weize</au><au>Zhai, Weixin</au><au>Wang, Xinxin</au><au>Chen, Zhibo</au><au>Wen, Long</au><au>Xu, Yuanyuan</au><au>Wu, Caicong</au><aucorp>1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China</aucorp><aucorp>2. Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, Beijing 100083, China</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone</atitle><jtitle>International journal of agricultural and biological engineering</jtitle><date>2022-07-01</date><risdate>2022</risdate><volume>15</volume><issue>4</issue><spage>154</spage><epage>162</epage><pages>154-162</pages><issn>1934-6344</issn><eissn>1934-6352</eissn><abstract>In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach, this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a smartphone. First, three driving modes were developed for maize sowing scenarios: manual driving assisted driving and unmanned driving. While sowing, smartphone software and CAN (Controller Area Network) storage devices collected both positional data and engine operating conditions. Second, the tractor trajectory points were divided into kinematic sequences, with six driving cycle indicators built in each series based on the time window. Based on the semantic information of the kinematic sequences, the three operations of sowing, seeds filling, and turning round were well recognized. Last, a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm, regarding three factors: driving cycles, operating behaviors, and driving patterns. When compared to the traditional K-means algorithm, the results demonstrated that the harmonic mean of the precision and recall (F1 score) of sowing behavior recognition, seeds filling behavior recognition, and turning behavior recognition were enhanced by 2.06%, 8.99%, and 21.79%, respectively. In terms of the impacts of driving modes and operating behaviors on fuel consumption, assisted driving mode had the lowest fuel usage for both sowing and turning behavior. Therefore, assisted driving is the most fuel-efficient mode for maize sowing. Combining the three driving modes, the relative error of the fuel consumption prediction model was 0.11 L/h, with the manual driving mode having the lowest relative error at 0.09 L/h. This research method lays the foundation for the optimization of tractor operation behavior, the selection of tractor driving mode, and the fine management of tractor fuel consumption.</abstract><cop>Beijing</cop><pub>International Journal of Agricultural and Biological Engineering (IJABE)</pub><doi>10.25165/j.ijabe.20221504.7454</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1934-6344 |
| ispartof | International journal of agricultural and biological engineering, 2022-07, Vol.15 (4), p.154-162 |
| issn | 1934-6344 1934-6352 |
| language | eng |
| recordid | cdi_proquest_journals_2716599882 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Agricultural equipment Algorithms Behavior Cellular telephones Controller area network Corn Data collection Electronic devices Farm machinery Fuel consumption Kinematics Neural networks Optimization Prediction models Recognition Seeds Sensors Sequences Smartphones Support vector machines Tractors Turning behavior Vehicles |
| title | Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T03%3A40%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Behavior%20recognition%20and%20fuel%20consumption%20prediction%20of%20tractor%20sowing%20operations%20using%20smartphone&rft.jtitle=International%20journal%20of%20agricultural%20and%20biological%20engineering&rft.au=Yang,%20Lili&rft.aucorp=1.%20College%20of%20Information%20and%20Electrical%20Engineering,%20China%20Agricultural%20University,%20Beijing%20100083,%20China&rft.date=2022-07-01&rft.volume=15&rft.issue=4&rft.spage=154&rft.epage=162&rft.pages=154-162&rft.issn=1934-6344&rft.eissn=1934-6352&rft_id=info:doi/10.25165/j.ijabe.20221504.7454&rft_dat=%3Cproquest_cross%3E2716599882%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2716599882&rft_id=info:pmid/&rfr_iscdi=true |