A precise crop row detection algorithm in complex farmland for unmanned agricultural machines
Crop row detection is an important part of precision agriculture. Therefore, an unmanned agricultural machine crop row detection method based on YOLO-R is proposed. This method can solve the problem that traditional image processing methods are easily affected by weeds, light and other factors when...
Uloženo v:
| Vydáno v: | Biosystems engineering Ročník 232; s. 1 - 12 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.08.2023
|
| Témata: | |
| ISSN: | 1537-5110 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Crop row detection is an important part of precision agriculture. Therefore, an unmanned agricultural machine crop row detection method based on YOLO-R is proposed. This method can solve the problem that traditional image processing methods are easily affected by weeds, light and other factors when extracting crop feature points. First, the YOLO-R object detection algorithm was used to obtain the crop position information, and then, the number of crop rows in the image and the crop in each crop row were obtained by the DBSCAN clustering algorithm. Finally, the function expression for each crop row was obtained by using the least squares method. The experimental results show that the AP values of YOLO-R are 91.69%, 95.34% and 89.13% on the seven-day, 14-day, and 21-day rice datasets, respectively. When the proposed algorithm's number of parameters was only 12.31% of that of YOLOv4 and the FPS was 17.54 higher than that of YOLOv4, the AP value was only 2.2% lower. The accuracy values of crop row detection algorithm are 93.91%, 95.87% and 89.87% on the seven-day, 14-day, and 21-day rice datasets, respectively, which indicates that the algorithm in this paper can effectively identify crop lines.
•Proposing a crop row detection method based on YOLO-R object detection algorithm.•Ghostnet and Focal Loss are used in YOLO-R to perform better and faster.•Use channel attention module to coordinate the importance between each channel.•Incremental ablation study on all network designs. |
|---|---|
| AbstractList | Crop row detection is an important part of precision agriculture. Therefore, an unmanned agricultural machine crop row detection method based on YOLO-R is proposed. This method can solve the problem that traditional image processing methods are easily affected by weeds, light and other factors when extracting crop feature points. First, the YOLO-R object detection algorithm was used to obtain the crop position information, and then, the number of crop rows in the image and the crop in each crop row were obtained by the DBSCAN clustering algorithm. Finally, the function expression for each crop row was obtained by using the least squares method. The experimental results show that the AP values of YOLO-R are 91.69%, 95.34% and 89.13% on the seven-day, 14-day, and 21-day rice datasets, respectively. When the proposed algorithm's number of parameters was only 12.31% of that of YOLOv4 and the FPS was 17.54 higher than that of YOLOv4, the AP value was only 2.2% lower. The accuracy values of crop row detection algorithm are 93.91%, 95.87% and 89.87% on the seven-day, 14-day, and 21-day rice datasets, respectively, which indicates that the algorithm in this paper can effectively identify crop lines.
•Proposing a crop row detection method based on YOLO-R object detection algorithm.•Ghostnet and Focal Loss are used in YOLO-R to perform better and faster.•Use channel attention module to coordinate the importance between each channel.•Incremental ablation study on all network designs. Crop row detection is an important part of precision agriculture. Therefore, an unmanned agricultural machine crop row detection method based on YOLO-R is proposed. This method can solve the problem that traditional image processing methods are easily affected by weeds, light and other factors when extracting crop feature points. First, the YOLO-R object detection algorithm was used to obtain the crop position information, and then, the number of crop rows in the image and the crop in each crop row were obtained by the DBSCAN clustering algorithm. Finally, the function expression for each crop row was obtained by using the least squares method. The experimental results show that the AP values of YOLO-R are 91.69%, 95.34% and 89.13% on the seven-day, 14-day, and 21-day rice datasets, respectively. When the proposed algorithm's number of parameters was only 12.31% of that of YOLOv4 and the FPS was 17.54 higher than that of YOLOv4, the AP value was only 2.2% lower. The accuracy values of crop row detection algorithm are 93.91%, 95.87% and 89.87% on the seven-day, 14-day, and 21-day rice datasets, respectively, which indicates that the algorithm in this paper can effectively identify crop lines. |
| Author | Cui, Shangqing Luo, Jiaqi Gao, Rui Ruan, Zhiwen Chang, Penghao Su, Zhongbin |
| Author_xml | – sequence: 1 givenname: Zhiwen surname: Ruan fullname: Ruan, Zhiwen – sequence: 2 givenname: Penghao surname: Chang fullname: Chang, Penghao – sequence: 3 givenname: Shangqing surname: Cui fullname: Cui, Shangqing – sequence: 4 givenname: Jiaqi surname: Luo fullname: Luo, Jiaqi – sequence: 5 givenname: Rui surname: Gao fullname: Gao, Rui email: 415730327@qq.com – sequence: 6 givenname: Zhongbin surname: Su fullname: Su, Zhongbin email: suzb001@163.com |
| BookMark | eNqNkT1PwzAQhj0UCVr4D5ZYWBrOcT7FVFXlQ6rEAiOyXOdcXCV2sB2g_55UZYGp0y3v--juuSmZWGeRkGsGCQNW3O6SjXFhHyJ2Ae02SSHlCRQJMJiQC5bzcp4zBudkGsIOgOVlVlyQtwXtPSoTkCrveurdF20woorGWSrbrfMmvnfUWKpc17f4TbX0XSttQ7XzdLCdtBYbKrfeqKGNg5ct7aR6NxbDJTnTsg149Ttn5PV-9bJ8nK-fH56Wi_Vc8QLivFESdQ6cQZ7WUKHOeMUa5LJmqsrTBnS2AdRlCrxUWQobBbxKea11mclclnxGbo7c3ruPAUMUnQkK23FNdEMQaVWVdc2rshqjd8foeG4IHrXovemk3wsG4iBS7MQfkeIgUkAhRpFje_GvrUyUB1fRS9OeyFgdGTga-TToRVAGrcLGjJ-IonHmJM4PshWgsg |
| CitedBy_id | crossref_primary_10_1117_1_JEI_33_2_023049 crossref_primary_10_3390_agriculture14010124 crossref_primary_10_3390_agronomy14081749 crossref_primary_10_3390_agronomy13112721 crossref_primary_10_3390_agriculture15121297 crossref_primary_10_3390_agriculture14070969 crossref_primary_10_3390_agriculture15090942 crossref_primary_10_1016_j_agwat_2023_108645 crossref_primary_10_3390_agriculture15070796 crossref_primary_10_1109_ACCESS_2024_3422422 crossref_primary_10_1016_j_jii_2025_100938 crossref_primary_10_1016_j_compag_2024_109871 crossref_primary_10_1016_j_atech_2025_101179 crossref_primary_10_1016_j_biosystemseng_2024_06_013 crossref_primary_10_1016_j_compag_2024_109776 crossref_primary_10_3390_app15147789 crossref_primary_10_1016_j_compag_2024_108923 crossref_primary_10_1016_j_biosystemseng_2024_02_014 crossref_primary_10_1016_j_biosystemseng_2024_07_016 crossref_primary_10_3390_agriculture14010057 crossref_primary_10_3390_machines12040218 crossref_primary_10_3389_fpls_2024_1338228 crossref_primary_10_1109_ACCESS_2024_3458442 crossref_primary_10_1016_j_compag_2024_109090 crossref_primary_10_3390_app14167285 crossref_primary_10_3390_agriculture15010002 crossref_primary_10_1007_s11119_025_10227_3 crossref_primary_10_1016_j_compag_2024_109086 crossref_primary_10_3390_drones8090490 crossref_primary_10_7717_peerj_cs_2477 crossref_primary_10_1016_j_chemolab_2024_105064 crossref_primary_10_1007_s11119_025_10243_3 crossref_primary_10_1007_s10462_024_10956_3 crossref_primary_10_1002_rob_22360 crossref_primary_10_3389_fpls_2024_1499896 crossref_primary_10_3390_fishes10020081 |
| Cites_doi | 10.1016/j.compag.2021.106054 10.1016/j.compag.2021.106135 10.1016/j.compag.2018.09.014 10.1016/j.ymssp.2020.106791 10.3390/s20185249 10.1016/j.compag.2022.107057 10.1016/j.compag.2022.107032 10.1016/j.compag.2016.02.002 10.1109/ACCESS.2019.2960873 10.1016/j.compag.2022.107412 10.1016/j.compag.2020.105766 10.1016/j.compag.2011.10.006 10.1016/j.biosystemseng.2021.08.030 10.1016/j.compag.2022.107429 10.1016/j.compag.2019.105203 10.1016/j.compag.2017.09.008 10.1016/j.biosystemseng.2017.01.013 |
| ContentType | Journal Article |
| Copyright | 2023 IAgrE |
| Copyright_xml | – notice: 2023 IAgrE |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.biosystemseng.2023.06.010 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Agriculture |
| EndPage | 12 |
| ExternalDocumentID | 10_1016_j_biosystemseng_2023_06_010 S1537511023001320 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 23N 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AACTN AAEDT AAEDW AAHBH AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AATLK AAXKI AAXUO ABFNM ABFRF ABGRD ABJNI ABMAC ABXDB ACDAQ ACGFO ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADQTV ADTZH AEBSH AECPX AEFWE AEKER AENEX AEQOU AFJKZ AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHJVU AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BJAXD BKOJK BLXMC CAG COF CS3 DM4 DU5 EBS EFBJH EJD EO8 EO9 EP2 EP3 FDB FEDTE FIRID FNPLU FYGXN G-Q GBLVA HVGLF HZ~ IHE J1W JJJVA K-O KOM LG5 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 PC. Q38 RIG ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SSA SST SSZ T5K UHS UNMZH ~G- ~KM 9DU AATTM AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFPUW AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c360t-dcaef5031052908ef4381de3a91c852d0f4b0ef72037c420bc038239ff74a5a73 |
| ISICitedReferencesCount | 41 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001037034800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1537-5110 |
| IngestDate | Mon Sep 29 02:28:30 EDT 2025 Tue Nov 18 22:29:57 EST 2025 Sat Nov 29 07:04:07 EST 2025 Tue Dec 03 03:45:04 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | YOLO-R object detection algorithm Crop row detection Least square method DBSCAN clustering algorithm |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c360t-dcaef5031052908ef4381de3a91c852d0f4b0ef72037c420bc038239ff74a5a73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 2887993878 |
| PQPubID | 24069 |
| PageCount | 12 |
| ParticipantIDs | proquest_miscellaneous_2887993878 crossref_primary_10_1016_j_biosystemseng_2023_06_010 crossref_citationtrail_10_1016_j_biosystemseng_2023_06_010 elsevier_sciencedirect_doi_10_1016_j_biosystemseng_2023_06_010 |
| PublicationCentury | 2000 |
| PublicationDate | August 2023 2023-08-00 20230801 |
| PublicationDateYYYYMMDD | 2023-08-01 |
| PublicationDate_xml | – month: 08 year: 2023 text: August 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Biosystems engineering |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Guo, He, Deng, Fan, Xu, Cao, Feng, Li, Wu, Lv, others (bib9) 2022; 199 Li, Li, Yang, Guo, Yang, Yue, Wang (bib17) 2021; 182 Liu, Yang, Zeng, Liu (bib20) 2020; 142 Aichen, Min, Qingshan, others (bib1) 2021; 37 Ren, He, Girshick, Sun (bib27) 2015; 28 Ge, Liu, Wang, Li, Sun (bib8) 2021 Han, Wang, Tian, Guo, Xu, Xu (bib10) 2020 Bochkovskiy, Wang, Liao (bib3) 2020 Hu, Liu, Zhao, Liu, Yang, Sun, Chen, Li, Zhou (bib14) 2021; 185 Redmon, Divvala, Girshick, Farhadi (bib26) 2016 Ponnambalam, Bakken, Moore, Glenn Omholt Gjevestad, Johan From (bib25) 2020; 20 Zehua, Xu, Xiuhao, Lintao, Hongwei, Zhicheng (bib32) 2018; 49 He, Gkioxari, Dollár, Girshick (bib11) 2017 Zhang, Li, Zhang, Zhou, Tian, Xiong, Gu (bib34) 2018; 154 Pérez-Ruiz, Slaughter, Gliever, Upadhyaya (bib24) 2012; 80 Zhang, Chen, Li (bib33) 2017; 143 Chen, Wang, Yang, Zhang, Cheng, Sun (bib5) 2021 Sun, Zhang, Jiang, Kong, Xu, Zhan, Tomizuka, Li, Yuan, Wang, others (bib28) 2021 Carion, Massa, Synnaeve, Usunier, Kirillov, Zagoruyko (bib4) 2020 Zhu, Su, Lu, Li, Wang, Dai (bib35) 2020 Ma, Tao, Du, Yu, Wu (bib22) 2021; 211 He, Zhang, Ren, Sun (bib12) 2015; 37 Liu, Anguelov, Erhan, Szegedy, Reed, Fu, Berg (bib19) 2016; 14 Man, Yuhan, Shichao, Ruyue, Hongzhen, Zhenqian (bib21) 2020; 51 Xie, Liao, Ma, Jin (bib31) 2018; 1 Jie, Jinguang, Zhigang, Xiwen, Yang, Lian (bib16) 2019; 50 Pang, Shi, Gao, Jiang, Veeranampalayam-Sivakumar, Thompson, Luck, Liu (bib23) 2020; 178 Lin, Goyal, Girshick, He, Dollár (bib18) 2017 García-Santillán, Montalvo, Guerrero, Pajares (bib7) 2017; 156 Wang, Yan, Meng, Yao, Han, Zhang (bib30) 2022; 198 Chen, Wang, Zhang, Luo, Wei, Long, Wang (bib6) 2022; 202 Wang, Kang, Chu, Liu (bib29) 2022; 202 Bah, Hafiane, Canals (bib2) 2019; 8 Jiang, Wang, Wang, Liu (bib15) 2016; 123 Huang, Wu, Sun, Ma, Jiang, Qi (bib13) 2020; 169 Liu (10.1016/j.biosystemseng.2023.06.010_bib20) 2020; 142 Pérez-Ruiz (10.1016/j.biosystemseng.2023.06.010_bib24) 2012; 80 Ge (10.1016/j.biosystemseng.2023.06.010_bib8) 2021 Zehua (10.1016/j.biosystemseng.2023.06.010_bib32) 2018; 49 Chen (10.1016/j.biosystemseng.2023.06.010_bib6) 2022; 202 Man (10.1016/j.biosystemseng.2023.06.010_bib21) 2020; 51 Zhang (10.1016/j.biosystemseng.2023.06.010_bib34) 2018; 154 Jie (10.1016/j.biosystemseng.2023.06.010_bib16) 2019; 50 Zhu (10.1016/j.biosystemseng.2023.06.010_bib35) 2020 Jiang (10.1016/j.biosystemseng.2023.06.010_bib15) 2016; 123 Redmon (10.1016/j.biosystemseng.2023.06.010_bib26) 2016 Zhang (10.1016/j.biosystemseng.2023.06.010_bib33) 2017; 143 Aichen (10.1016/j.biosystemseng.2023.06.010_bib1) 2021; 37 Sun (10.1016/j.biosystemseng.2023.06.010_bib28) 2021 Carion (10.1016/j.biosystemseng.2023.06.010_bib4) 2020 Ren (10.1016/j.biosystemseng.2023.06.010_bib27) 2015; 28 Guo (10.1016/j.biosystemseng.2023.06.010_bib9) 2022; 199 Bochkovskiy (10.1016/j.biosystemseng.2023.06.010_bib3) 2020 Liu (10.1016/j.biosystemseng.2023.06.010_bib19) 2016; 14 Ponnambalam (10.1016/j.biosystemseng.2023.06.010_bib25) 2020; 20 Lin (10.1016/j.biosystemseng.2023.06.010_bib18) 2017 Han (10.1016/j.biosystemseng.2023.06.010_bib10) 2020 He (10.1016/j.biosystemseng.2023.06.010_bib12) 2015; 37 Pang (10.1016/j.biosystemseng.2023.06.010_bib23) 2020; 178 Huang (10.1016/j.biosystemseng.2023.06.010_bib13) 2020; 169 Bah (10.1016/j.biosystemseng.2023.06.010_bib2) 2019; 8 Wang (10.1016/j.biosystemseng.2023.06.010_bib30) 2022; 198 García-Santillán (10.1016/j.biosystemseng.2023.06.010_bib7) 2017; 156 Li (10.1016/j.biosystemseng.2023.06.010_bib17) 2021; 182 Hu (10.1016/j.biosystemseng.2023.06.010_bib14) 2021; 185 Chen (10.1016/j.biosystemseng.2023.06.010_bib5) 2021 Xie (10.1016/j.biosystemseng.2023.06.010_bib31) 2018; 1 He (10.1016/j.biosystemseng.2023.06.010_bib11) 2017 Ma (10.1016/j.biosystemseng.2023.06.010_bib22) 2021; 211 Wang (10.1016/j.biosystemseng.2023.06.010_bib29) 2022; 202 |
| References_xml | – volume: 156 start-page: 61 year: 2017 end-page: 79 ident: bib7 article-title: Automatic detection of curved and straight crop rows from images in maize fields publication-title: Biosystems Engineering – volume: 50 year: 2019 ident: bib16 article-title: Design and experiment of automatic operation system for rice transplanter publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery – volume: 202 year: 2022 ident: bib29 article-title: Deep learning-based automatic dairy cow ocular surface temperature detection from thermal images publication-title: Computers and Electronics in Agriculture – volume: 185 year: 2021 ident: bib14 article-title: Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network publication-title: Computers and Electronics in Agriculture – year: 2020 ident: bib35 article-title: Deformable detr: Deformable transformers for end-to-end object detection publication-title: ArXiv Preprint ArXiv:2010.04159 – volume: 142 year: 2020 ident: bib20 article-title: Bending diagnosis of rice seedling lines and guidance line extraction of automatic weeding equipment in paddy field publication-title: Mechanical Systems and Signal Processing – start-page: 14454 year: 2021 end-page: 14463 ident: bib28 article-title: Sparse r-cnn: End-to-end object detection with learnable proposals publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 37 start-page: 1904 year: 2015 end-page: 1916 ident: bib12 article-title: Spatial pyramid pooling in deep convolutional networks for visual recognition publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 2980 year: 2017 end-page: 2988 ident: bib18 article-title: Focal loss for dense object detection publication-title: Proceedings of the IEEE International Conference on Computer Vision – volume: 49 year: 2018 ident: bib32 article-title: Research progress of rice transplanting mechanization publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery – volume: 202 year: 2022 ident: bib6 article-title: Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion publication-title: Computers and Electronics in Agriculture – start-page: 213 year: 2020 end-page: 229 ident: bib4 article-title: End-to-end object detection with transformers publication-title: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16 – volume: 143 start-page: 66 year: 2017 end-page: 78 ident: bib33 article-title: A visual navigation algorithm for paddy field weeding robot based on image understanding publication-title: Computers and Electronics in Agriculture – volume: 8 start-page: 5189 year: 2019 end-page: 5200 ident: bib2 article-title: CRowNet: Deep network for crop row detection in UAV images publication-title: IEEE Access – start-page: 2961 year: 2017 end-page: 2969 ident: bib11 article-title: Mask r-cnn publication-title: Proceedings of the IEEE International Conference on Computer Vision – start-page: 779 year: 2016 end-page: 788 ident: bib26 article-title: You only look once: Unified, real-time object detection publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 211 start-page: 63 year: 2021 end-page: 76 ident: bib22 article-title: Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method publication-title: Biosystems Engineering – volume: 1 start-page: 199 year: 2018 end-page: 208 ident: bib31 article-title: Gated feature pyramid network for object detection publication-title: Pattern Recognition and Computer Vision: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV – volume: 37 start-page: 202 year: 2021 end-page: 210 ident: bib1 article-title: Seedling crop row extraction method based on regional growth and mean shift clustering publication-title: Transactions of the Chinese Society of Agricultural Engineering – volume: 14 start-page: 21 year: 2016 end-page: 37 ident: bib19 article-title: Ssd: Single shot multibox detector publication-title: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I – volume: 80 start-page: 41 year: 2012 end-page: 49 ident: bib24 article-title: Automatic GPS-based intra-row weed knife control system for transplanted row crops publication-title: Computers and Electronics in Agriculture – volume: 20 start-page: 5249 year: 2020 ident: bib25 article-title: Autonomous crop row guidance using adaptive multi-roi in strawberry fields publication-title: Sensors – volume: 28 year: 2015 ident: bib27 article-title: Faster r-cnn: Towards real-time object detection with region proposal networks publication-title: Advances in Neural Information Processing Systems – start-page: 1580 year: 2020 end-page: 1589 ident: bib10 article-title: Ghostnet: More features from cheap operations publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 169 year: 2020 ident: bib13 article-title: Deep localization model for intra-row crop detection in paddy field publication-title: Computers and Electronics in Agriculture – volume: 51 year: 2020 ident: bib21 article-title: Research progress of agricultural machinery navigation technology publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery – volume: 154 start-page: 165 year: 2018 end-page: 175 ident: bib34 article-title: Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method publication-title: Computers and Electronics in Agriculture – year: 2021 ident: bib8 article-title: Yolox: Exceeding yolo series in 2021 publication-title: arXiv – volume: 198 year: 2022 ident: bib30 article-title: DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection publication-title: Computers and Electronics in Agriculture – start-page: 13039 year: 2021 end-page: 13048 ident: bib5 article-title: You only look one-level feature publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition – volume: 123 start-page: 211 year: 2016 end-page: 223 ident: bib15 article-title: Wheat rows detection at the early growth stage based on Hough transform and vanishing point publication-title: Computers and Electronics in Agriculture – year: 2020 ident: bib3 article-title: Yolov4: Optimal speed and accuracy of object detection publication-title: arXiv – volume: 199 year: 2022 ident: bib9 article-title: Pigeon cleaning behavior detection algorithm based on light-weight network publication-title: Computers and Electronics in Agriculture – volume: 178 year: 2020 ident: bib23 article-title: Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery publication-title: Computers and Electronics in Agriculture – volume: 182 year: 2021 ident: bib17 article-title: A high-precision detection method of hydroponic lettuce seedlings status based on improved Faster RCNN publication-title: Computers and Electronics in Agriculture – start-page: 14454 year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib28 article-title: Sparse r-cnn: End-to-end object detection with learnable proposals – volume: 182 year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib17 article-title: A high-precision detection method of hydroponic lettuce seedlings status based on improved Faster RCNN publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106054 – year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib3 article-title: Yolov4: Optimal speed and accuracy of object detection publication-title: arXiv – volume: 37 start-page: 202 issue: 19 year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib1 article-title: Seedling crop row extraction method based on regional growth and mean shift clustering publication-title: Transactions of the Chinese Society of Agricultural Engineering – volume: 185 year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib14 article-title: Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2021.106135 – volume: 154 start-page: 165 year: 2018 ident: 10.1016/j.biosystemseng.2023.06.010_bib34 article-title: Automated robust crop-row detection in maize fields based on position clustering algorithm and shortest path method publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.09.014 – volume: 142 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib20 article-title: Bending diagnosis of rice seedling lines and guidance line extraction of automatic weeding equipment in paddy field publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2020.106791 – volume: 20 start-page: 5249 issue: 18 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib25 article-title: Autonomous crop row guidance using adaptive multi-roi in strawberry fields publication-title: Sensors doi: 10.3390/s20185249 – volume: 198 year: 2022 ident: 10.1016/j.biosystemseng.2023.06.010_bib30 article-title: DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.107057 – volume: 199 year: 2022 ident: 10.1016/j.biosystemseng.2023.06.010_bib9 article-title: Pigeon cleaning behavior detection algorithm based on light-weight network publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.107032 – volume: 123 start-page: 211 year: 2016 ident: 10.1016/j.biosystemseng.2023.06.010_bib15 article-title: Wheat rows detection at the early growth stage based on Hough transform and vanishing point publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2016.02.002 – volume: 8 start-page: 5189 year: 2019 ident: 10.1016/j.biosystemseng.2023.06.010_bib2 article-title: CRowNet: Deep network for crop row detection in UAV images publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2960873 – volume: 202 year: 2022 ident: 10.1016/j.biosystemseng.2023.06.010_bib6 article-title: Weed detection in sesame fields using a YOLO model with an enhanced attention mechanism and feature fusion publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.107412 – year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib35 article-title: Deformable detr: Deformable transformers for end-to-end object detection publication-title: ArXiv Preprint ArXiv:2010.04159 – start-page: 2980 year: 2017 ident: 10.1016/j.biosystemseng.2023.06.010_bib18 article-title: Focal loss for dense object detection – volume: 49 issue: 5 year: 2018 ident: 10.1016/j.biosystemseng.2023.06.010_bib32 article-title: Research progress of rice transplanting mechanization publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery – start-page: 2961 year: 2017 ident: 10.1016/j.biosystemseng.2023.06.010_bib11 article-title: Mask r-cnn – volume: 178 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib23 article-title: Improved crop row detection with deep neural network for early-season maize stand count in UAV imagery publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2020.105766 – volume: 51 issue: 4 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib21 article-title: Research progress of agricultural machinery navigation technology publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery – volume: 14 start-page: 21 year: 2016 ident: 10.1016/j.biosystemseng.2023.06.010_bib19 article-title: Ssd: Single shot multibox detector – start-page: 13039 year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib5 article-title: You only look one-level feature – volume: 37 start-page: 1904 year: 2015 ident: 10.1016/j.biosystemseng.2023.06.010_bib12 article-title: Spatial pyramid pooling in deep convolutional networks for visual recognition – start-page: 1580 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib10 article-title: Ghostnet: More features from cheap operations – volume: 28 year: 2015 ident: 10.1016/j.biosystemseng.2023.06.010_bib27 article-title: Faster r-cnn: Towards real-time object detection with region proposal networks publication-title: Advances in Neural Information Processing Systems – volume: 80 start-page: 41 year: 2012 ident: 10.1016/j.biosystemseng.2023.06.010_bib24 article-title: Automatic GPS-based intra-row weed knife control system for transplanted row crops publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2011.10.006 – year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib8 article-title: Yolox: Exceeding yolo series in 2021 publication-title: arXiv – volume: 50 issue: 3 year: 2019 ident: 10.1016/j.biosystemseng.2023.06.010_bib16 article-title: Design and experiment of automatic operation system for rice transplanter publication-title: Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery – volume: 1 start-page: 199 year: 2018 ident: 10.1016/j.biosystemseng.2023.06.010_bib31 article-title: Gated feature pyramid network for object detection – start-page: 213 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib4 article-title: End-to-end object detection with transformers – volume: 211 start-page: 63 year: 2021 ident: 10.1016/j.biosystemseng.2023.06.010_bib22 article-title: Automatic detection of crop root rows in paddy fields based on straight-line clustering algorithm and supervised learning method publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2021.08.030 – volume: 202 year: 2022 ident: 10.1016/j.biosystemseng.2023.06.010_bib29 article-title: Deep learning-based automatic dairy cow ocular surface temperature detection from thermal images publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2022.107429 – start-page: 779 year: 2016 ident: 10.1016/j.biosystemseng.2023.06.010_bib26 article-title: You only look once: Unified, real-time object detection – volume: 169 year: 2020 ident: 10.1016/j.biosystemseng.2023.06.010_bib13 article-title: Deep localization model for intra-row crop detection in paddy field publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2019.105203 – volume: 143 start-page: 66 year: 2017 ident: 10.1016/j.biosystemseng.2023.06.010_bib33 article-title: A visual navigation algorithm for paddy field weeding robot based on image understanding publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2017.09.008 – volume: 156 start-page: 61 year: 2017 ident: 10.1016/j.biosystemseng.2023.06.010_bib7 article-title: Automatic detection of curved and straight crop rows from images in maize fields publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2017.01.013 |
| SSID | ssj0015746 |
| Score | 2.5431252 |
| Snippet | Crop row detection is an important part of precision agriculture. Therefore, an unmanned agricultural machine crop row detection method based on YOLO-R is... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | agricultural land agricultural machinery and equipment algorithms Crop row detection data collection DBSCAN clustering algorithm Least square method precision agriculture rice YOLO-R object detection algorithm |
| Title | A precise crop row detection algorithm in complex farmland for unmanned agricultural machines |
| URI | https://dx.doi.org/10.1016/j.biosystemseng.2023.06.010 https://www.proquest.com/docview/2887993878 |
| Volume | 232 |
| WOSCitedRecordID | wos001037034800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 1537-5110 databaseCode: AIEXJ dateStart: 20020101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0015746 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3ra9RAEF-OVkQ_iE-sL1b0W0jJ5XGb-EEIpaIipWCFQ5CwSTa9lLukzSX1_gv_ZWeyj6SVwin4JVz22LnNzS-zj_nNDCFvI78IRcbh_Q6whBnnwk7TlNkMDLLrp3Cvik2wo6NwPo-OJ5NfOhbmcsmqKtxsovP_qmpoA2Vj6OxfqNsIhQb4DEqHK6gdrlspPsa4f6yVYWF1Lgu22VYuWqFKgi9P66ZsFytL8c-XYmMVvFkhwbGnHHbViqPttfhpM-TlWPWcS8U31D7gspZ5oNeWGLIaGg9OJ49Wvy_Kn0O42YE-nz6GLgtem_auZxV8xa8vRlK-dNIzVPKLcnxA4XqGHjfYVGbDus4ZG11XnWpKszkdzb-SVf2HZZeHDGf7qXk2GOc-_l6ff1VJv5JP-9o8Z9iHmth2llwRlqCwBGl-GLK367IgAjO5G386nH82jqmAyYA1_Uy3yZuBMnjj2G5a9Fyb_vs1zcl9ck9tRmgsQfSATET1kNyNjeIF3I0SVj4iP2KqwEURXBTARQ24qAEXLSuqwEU1uCiAi2pw0TG4qAbXY_Ltw-HJwUdbFeiwM2_mtHaecVEEmFw2cCMnFAXmi8uFx6NpFgZu7hR-6ogCPf0s810nzRx0O6ObwOcBZ94TslPVlXiKqQO80MlnWO0g96cCNgZ5KoSThwUWqg9ne-Sd_geTTGWvxyIqy2QLbe4R33Q-l0lctuv2XqsqUetRuc5MAJTbCXitFZyA1UZXHK9E3a0TF-Z22BmELHz2b2N7Tu4M79oLstM2nXhJbmWXbbluXinU_gaTKMgU |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+precise+crop+row+detection+algorithm+in+complex+farmland+for+unmanned+agricultural+machines&rft.jtitle=Biosystems+engineering&rft.au=Ruan%2C+Zhiwen&rft.au=Chang%2C+Penghao&rft.au=Cui%2C+Shangqing&rft.au=Luo%2C+Jiaqi&rft.date=2023-08-01&rft.issn=1537-5110&rft.volume=232&rft.spage=1&rft.epage=12&rft_id=info:doi/10.1016%2Fj.biosystemseng.2023.06.010&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_biosystemseng_2023_06_010 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1537-5110&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1537-5110&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1537-5110&client=summon |