Use of Open-Source Object Detection Algorithms to Detect Palmer Amaranth (Amaranthus palmeri) in Soybean
Site-specific weed management using open-source object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth (Amaranthus palmeri S. Watson) in soybean [Glycine max (L.) Merr.]. The objectives were to (...
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| Vydáno v: | Weed science Ročník 70; číslo 6; s. 648 - 662 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, USA
The Weed Science Society of America
01.11.2022
Cambridge University Press |
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| ISSN: | 0043-1745, 1550-2759 |
| On-line přístup: | Získat plný text |
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| Abstract | Site-specific weed management using open-source object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth (Amaranthus palmeri S. Watson) in soybean [Glycine max (L.) Merr.]. The objectives were to (1) develop an annotated image database of A. palmeri and soybean to fine-tune object detection algorithms, (2) compare effectiveness of multiple open-source algorithms in detecting A. palmeri, and (3) evaluate the relationship between A. palmeri growth features and A. palmeri detection ability. Soybean field sites were established in Manhattan, KS, and Gypsum, KS, with natural populations of A. palmeri. A total of 1,108 and 392 images were taken aerially and at ground level, respectively, between May 27 and July 27, 2021. After image annotation, a total of 4,492 images were selected. Annotated images were used to fine-tune open-source faster regional convolutional (Faster R-CNN) and single-shot detector (SSD) algorithms using a Resnet backbone, as well as the “You Only Look Once” (YOLO) series algorithms. Results demonstrated that YOLO v. 5 achieved the highest mean average precision score of 0.77. For both A. palmeri and soybean detections within this algorithm, the highest F1 score was 0.72 when using a confidence threshold of 0.298. A lower confidence threshold of 0.15 increased the likelihood of species detection, but also increased the likelihood of false-positive detections. The trained YOLOv5 data set was used to identify A. palmeri in a data set paired with measured growth features. Linear regression models predicted that as A. palmeri densities increased and as A. palmeri height increased, precision, recall, and F1 scores of algorithms would decrease. We conclude that open-source algorithms such as YOLOv5 show great potential in detecting A. palmeri in soybean-cropping systems. |
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| AbstractList | Site-specific weed management using open-source object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth (Amaranthus palmeri S. Watson) in soybean [Glycine max (L.) Merr.]. The objectives were to (1) develop an annotated image database of A. palmeri and soybean to fine-tune object detection algorithms, (2) compare effectiveness of multiple open-source algorithms in detecting A. palmeri, and (3) evaluate the relationship between A. palmeri growth features and A. palmeri detection ability. Soybean field sites were established in Manhattan, KS, and Gypsum, KS, with natural populations of A. palmeri. A total of 1,108 and 392 images were taken aerially and at ground level, respectively, between May 27 and July 27, 2021. After image annotation, a total of 4,492 images were selected. Annotated images were used to fine-tune open-source faster regional convolutional (Faster R-CNN) and single-shot detector (SSD) algorithms using a Resnet backbone, as well as the “You Only Look Once” (YOLO) series algorithms. Results demonstrated that YOLO v. 5 achieved the highest mean average precision score of 0.77. For both A. palmeri and soybean detections within this algorithm, the highest F1 score was 0.72 when using a confidence threshold of 0.298. A lower confidence threshold of 0.15 increased the likelihood of species detection, but also increased the likelihood of false-positive detections. The trained YOLOv5 data set was used to identify A. palmeri in a data set paired with measured growth features. Linear regression models predicted that as A. palmeri densities increased and as A. palmeri height increased, precision, recall, and F1 scores of algorithms would decrease. We conclude that open-source algorithms such as YOLOv5 show great potential in detecting A. palmeri in soybean-cropping systems. Site-specific weed management using open-source object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of object detection algorithms to detect Palmer amaranth ( Amaranthus palmeri S. Watson) in soybean [ Glycine max (L.) Merr.]. The objectives were to (1) develop an annotated image database of A. palmeri and soybean to fine-tune object detection algorithms, (2) compare effectiveness of multiple open-source algorithms in detecting A. palmeri , and (3) evaluate the relationship between A. palmeri growth features and A. palmeri detection ability. Soybean field sites were established in Manhattan, KS, and Gypsum, KS, with natural populations of A. palmeri . A total of 1,108 and 392 images were taken aerially and at ground level, respectively, between May 27 and July 27, 2021. After image annotation, a total of 4,492 images were selected. Annotated images were used to fine-tune open-source faster regional convolutional (Faster R-CNN) and single-shot detector (SSD) algorithms using a Resnet backbone, as well as the “You Only Look Once” (YOLO) series algorithms. Results demonstrated that YOLO v. 5 achieved the highest mean average precision score of 0.77. For both A. palmeri and soybean detections within this algorithm, the highest F1 score was 0.72 when using a confidence threshold of 0.298. A lower confidence threshold of 0.15 increased the likelihood of species detection, but also increased the likelihood of false-positive detections. The trained YOLOv5 data set was used to identify A. palmeri in a data set paired with measured growth features. Linear regression models predicted that as A. palmeri densities increased and as A. palmeri height increased, precision, recall, and F1 scores of algorithms would decrease. We conclude that open-source algorithms such as YOLOv5 show great potential in detecting A. palmeri in soybean-cropping systems. |
| Author | Dille, J. Anita Barnhart, Isaac H. Goodin, Douglas Lancaster, Sarah Spotanski, Jess |
| Author_xml | – sequence: 1 givenname: Isaac H. orcidid: 0000-0002-3740-6771 surname: Barnhart fullname: Barnhart, Isaac H. organization: Graduate Research Assistant, Department of Agronomy, Kansas State University, Manhattan, KS, USA – sequence: 2 givenname: Sarah orcidid: 0000-0002-5818-0783 surname: Lancaster fullname: Lancaster, Sarah organization: Assistant Professor, Extension Weed Specialist, Department of Agronomy, Kansas State University, Manhattan, KS, USA – sequence: 3 givenname: Douglas orcidid: 0000-0003-0062-924X surname: Goodin fullname: Goodin, Douglas organization: Professor, Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA – sequence: 4 givenname: Jess surname: Spotanski fullname: Spotanski, Jess organization: Owner/Manager, Midwest Research Incorporated, York, NE, USA – sequence: 5 givenname: J. Anita orcidid: 0000-0002-3968-30860 surname: Dille fullname: Dille, J. Anita organization: Professor, Department of Agronomy, Kansas State University, Manhattan, KS, USA |
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| CitedBy_id | crossref_primary_10_1002_ps_70009 crossref_primary_10_1016_j_eswa_2025_127018 crossref_primary_10_1017_wsc_2024_63 crossref_primary_10_1016_j_cropro_2023_106522 crossref_primary_10_1016_j_chemolab_2024_105064 crossref_primary_10_1016_j_compag_2024_108622 crossref_primary_10_1002_csc2_21028 crossref_primary_10_3390_fire7120444 |
| Cites_doi | 10.3390/electronics10040372 10.1007/978-981-16-3880-0_35 10.1007/978-3-319-54193-8_13 10.1017/wet.2020.90 10.1017/wsc.2018.66 10.1007/s10489-021-02893-3 10.5586/aa.2011.068 10.3390/agriengineering2030032 10.1111/wre.12307 10.1088/1742-6596/1544/1/012033 10.1002/ps.6656 10.1111/j.1365-3180.2004.00423.x 10.1016/j.rse.2018.11.032 10.2135/cropsci1971.0011183X001100060051x 10.1007/978-3-319-10602-1_48 10.1109/ICCUBEA.2018.8697819 10.1614/WS-04-071R2 10.1109/CVPR.2017.351 10.1002/ps.6804 10.1109/CAIBDA53561.2021.00016 10.37221/eaef.13.2_42 10.1016/j.neucom.2017.01.018 10.1017/wet.2020.99 10.1109/CVPR.2016.91 10.1109/CVPR.2016.90 10.1002/ps.4009 10.1017/wet.2020.91 10.1007/978-3-319-46448-0_2 10.3390/agriengineering2020024 10.18280/ts.380211 10.3390/agronomy10071044 10.1609/aaai.v31i1.11231 10.3390/rs13010054 10.1023/B:PRAG.0000040806.39604.aa 10.1109/AVSS.2017.8078518 10.3390/s21134350 10.3390/rs12244091 10.3390/s20030578 10.1016/j.compag.2019.104963 10.1111/wre.12374 10.1016/j.compag.2021.106081 10.23919/MVA.2017.7986913 10.1038/s41598-020-66505-9 10.1109/CVPR.2017.211 10.1109/ICEngTechnol.2017.8308186 10.1017/S0043174500092997 10.1109/ICACCS48705.2020.9074315 10.1109/ICCE48956.2021.9352073 10.1109/CSCI46756.2018.00067 10.1109/DICTA.2007.4426799 10.1186/s40537-019-0197-0 10.1186/s13007-020-00570-z 10.1109/ICCV.2015.169 10.1109/DLS49591.2019.00011 10.1109/CVPR.2014.81 10.1614/WS-09-074.1 10.1109/LSP.2018.2889273 10.3390/rs12132136 10.1016/j.compag.2021.106040 10.1016/j.fcr.2019.02.022 10.1017/wsc.2020.46 10.1109/TPAMI.2016.2577031 10.1007/s11119-019-09666-6 |
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| Copyright | The Author(s), 2022. Published by Cambridge University Press on behalf of the Weed Science Society of America. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. The Author(s), 2022. Published by Cambridge University Press on behalf of the Weed Science Society of America The Author(s), 2022. Published by Cambridge University Press on behalf of the Weed Science Society of America. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | site-specific weed management YOLOv5 Single Shot Detector Artificial intelligence Faster R-CNN TensorFlow |
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| References | 2004; 44 2021; 21 2019; 6 2020; 20 2015; 72 2019; 59 2015; 11 1997; 45 2002; 8 2021; 182 2020; 16 2021; 184 2004; 5 2020; 13 2020; 12 2020; 10 2018; 67 2017; 235 2019; 221 1994; 42 2021; 35 2021; 13 1971; 11 2021; 38 2009; 57 2021; 10 2020; 2 2022; 2022 2017; 39 2019; 26 2022; 78 2011; 64 2005; 53 2019; 235 2022; 52 2020; 68 2020; 21 2018; 58 2020; 1544 Barroso (S0043174522000534_ref10) 2004; 44 Osborne (S0043174522000534_ref61) 2002; 8 Wenkel (S0043174522000534_ref95) 2021; 21 S0043174522000534_ref77 Naghashzadeh (S0043174522000534_ref59) 2015; 11 S0043174522000534_ref79 S0043174522000534_ref78 Yu (S0043174522000534_ref99) 2020; 68 Bongiovanni (S0043174522000534_ref13) 2004; 5 S0043174522000534_ref72 S0043174522000534_ref71 Parico (S0043174522000534_ref64) 2020; 13 Gao (S0043174522000534_ref27) 2020; 16 Du (S0043174522000534_ref21) 2020; 1544 S0043174522000534_ref88 S0043174522000534_ref87 S0043174522000534_ref86 S0043174522000534_ref85 Sharpe (S0043174522000534_ref76) 2020; 21 S0043174522000534_ref89 S0043174522000534_ref80 S0043174522000534_ref83 Hussain (S0043174522000534_ref39) 2020; 12 S0043174522000534_ref55 S0043174522000534_ref54 S0043174522000534_ref52 S0043174522000534_ref58 S0043174522000534_ref57 Ahmad (S0043174522000534_ref2) 2021; 184 S0043174522000534_ref51 S0043174522000534_ref50 Arsenijevic (S0043174522000534_ref8) 2021; 35 Maxwell (S0043174522000534_ref56) 2005; 53 S0043174522000534_ref66 S0043174522000534_ref65 Yang (S0043174522000534_ref97) 2019; 235 S0043174522000534_ref63 S0043174522000534_ref69 S0043174522000534_ref68 S0043174522000534_ref60 Sapkota (S0043174522000534_ref73) 2020; 2 Cardina (S0043174522000534_ref15) 1997; 45 S0043174522000534_ref33 S0043174522000534_ref32 Biffi (S0043174522000534_ref11) 2021; 13 Jin (S0043174522000534_ref41) 2022; 78 S0043174522000534_ref31 S0043174522000534_ref30 S0043174522000534_ref37 S0043174522000534_ref36 S0043174522000534_ref35 S0043174522000534_ref1 Li (S0043174522000534_ref49) 2020; 20 S0043174522000534_ref34 Sivakumar (S0043174522000534_ref81) 2020; 12 Ren (S0043174522000534_ref70) 2017; 39 Hussain (S0043174522000534_ref38) 2021; 182 Fehr (S0043174522000534_ref23) 1971; 11 Evans (S0043174522000534_ref22) 2015; 72 S0043174522000534_ref44 S0043174522000534_ref43 S0043174522000534_ref42 S0043174522000534_ref48 S0043174522000534_ref47 S0043174522000534_ref4 S0043174522000534_ref3 S0043174522000534_ref6 Klingaman (S0043174522000534_ref46) 1994; 42 S0043174522000534_ref5 S0043174522000534_ref7 S0043174522000534_ref9 S0043174522000534_ref19 S0043174522000534_ref18 S0043174522000534_ref16 S0043174522000534_ref96 S0043174522000534_ref14 S0043174522000534_ref101 Zhong (S0043174522000534_ref100) 2019; 221 S0043174522000534_ref12 S0043174522000534_ref91 S0043174522000534_ref90 Ying (S0043174522000534_ref98) 2021; 38 Fernández-Quintanilla (S0043174522000534_ref24) 2018; 58 S0043174522000534_ref94 S0043174522000534_ref93 Osorio (S0043174522000534_ref62) 2020; 2 S0043174522000534_ref92 Jha (S0043174522000534_ref40) 2009; 57 S0043174522000534_ref29 Sharpe (S0043174522000534_ref75) 2020; 10 S0043174522000534_ref28 Sun (S0043174522000534_ref84) 2022; 52 (S0043174522000534_ref67) 2021 Linn (S0043174522000534_ref53) 2019; 59 S0043174522000534_ref20 S0043174522000534_ref26 S0043174522000534_ref25 Kieloch (S0043174522000534_ref45) 2011; 64 Sharpe (S0043174522000534_ref74) 2018; 67 Somerville (S0043174522000534_ref82) 2020; 10 Chen (S0043174522000534_ref17) 2021; 10 |
| References_xml | – volume: 44 start-page: 460 year: 2004 end-page: 468 article-title: Simulating the effects of weed spatial pattern and resolution of mapping and spraying on economics of site-specific management publication-title: Weed Res – volume: 53 start-page: 221 year: 2005 end-page: 227 article-title: Justification for site-specific weed management based on ecology and economics publication-title: Weed Sci – volume: 35 start-page: 136 year: 2021 end-page: 143 publication-title: Weed Technol – volume: 6 start-page: 60 year: 2019 article-title: A survey on image data augmentation for deep learning publication-title: J Big Data – volume: 10 start-page: 1044 year: 2020 article-title: Spatial modelling of within-field weed populations: a review publication-title: Agronomy – volume: 1544 start-page: 012033 year: 2020 article-title: Overview of two-stage object detection algorithms publication-title: J Phys Conf Ser – volume: 26 start-page: 2 year: 2019 article-title: One-class convolutional neural network publication-title: IEEE Signal Proc Let – volume: 12 start-page: 4091 year: 2020 article-title: Design and development of a smart variable rate sprayer using deep learning publication-title: Remote Sens – volume: 21 start-page: 264 year: 2020 end-page: 277 article-title: Vegetation detection and discrimination within vegetable plasticulture row-middles using a convolutional neural network publication-title: Precis Agric – volume: 21 start-page: 4350 year: 2021 article-title: Confidence score: the forgotten dimension of object detection performance evaluation publication-title: Sensors – volume: 5 start-page: 359 year: 2004 end-page: 387 article-title: Precision agriculture and sustainability publication-title: Precis Agric – volume: 11 start-page: 90 year: 2015 end-page: 92 article-title: Broad-leaved weeds in chickpea (Cicer arietinum L.) as affected by plant density and Lentagran herbicide application publication-title: Electron J Biol – volume: 13 start-page: 42 year: 2020 end-page: 48 article-title: An aerial weed detection system for green onion crops using the you only look once (YOLOv3) deep learning algorithm publication-title: Eng Agric Environ Food – volume: 11 start-page: 929 year: 1971 end-page: 931 article-title: Stage of development descriptions for soybeans, Glycine max (L.) Merrill publication-title: Crop Sci – volume: 235 start-page: 142 year: 2019 end-page: 153 article-title: Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images publication-title: Field Crop Res – volume: 42 start-page: 523 year: 1994 end-page: 527 article-title: Palmer amaranth (Amaranthus palmeri) interference in soybeans (Glycine max). Weed publication-title: Sci – volume: 10 start-page: 9548 year: 2020 article-title: Goosegrass detection in strawberry and tomato using a convolutional neural network publication-title: Sci Rep – volume: 67 start-page: 239 year: 2018 end-page: 245 article-title: Detection of Carolina geranium (Geranium carolinianum) growing in competition with strawberry using convolutional neural networks publication-title: Weed Sci – volume: 52 start-page: 8448 year: 2022 end-page: 8463 article-title: RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring publication-title: Appl Intell – volume: 2 start-page: 350 year: 2020 end-page: 366 article-title: Mapping and estimating weeds in cotton using unmanned aerial systems-borne imagery publication-title: AgriEngineering – volume: 235 start-page: 228 year: 2017 end-page: 235 article-title: Plant identification using deep neural networks via optimization of transfer learning parameters publication-title: Neurocomputing – volume: 20 start-page: 578 year: 2020 article-title: A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network publication-title: Sensors – volume: 39 start-page: 1137 year: 2017 end-page: 1149 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE T Pattern Anal – volume: 59 start-page: 357 year: 2019 end-page: 366 article-title: In-field classification of herbicide-resistant Papaver rhoeas and Stellaria media using an imaging sensor of the maximum quantum efficiency of photosystem II publication-title: Weed Res – volume: 2022 start-page: 521 year: 2022 end-page: 529 article-title: Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat publication-title: Pest Manag Sci – volume: 45 start-page: 364 year: 1997 end-page: 373 article-title: The nature and consequence of weed spatial distribution publication-title: Weed Sci – volume: 35 start-page: 210 year: 2021 end-page: 215 article-title: Influence of sulfentrazone and metribuzin applied preemergence on soybean development and yield publication-title: Weed Technol – volume: 2 start-page: 471 year: 2020 end-page: 488 article-title: A deep learning approach for weed detection in lettuce crops using multispectral images publication-title: AgriEngineering – volume: 12 start-page: 2136 year: 2020 article-title: Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery publication-title: Remote Sens – volume: 184 start-page: 106081 year: 2021 article-title: Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems publication-title: Comput Electron Agric – volume: 13 start-page: 54 year: 2021 publication-title: Remote Sens – volume: 68 start-page: 545 year: 2020 end-page: 552 article-title: Detection of grassy weeds in bermudagrass with deep convolutional neural networks publication-title: Weed Sci – volume: 78 start-page: 1861 year: 2022 end-page: 1869 article-title: A novel deep learning-based method for detection of weeds in vegetables publication-title: Pest Manag Sci – volume: 16 start-page: 29 year: 2020 article-title: Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields publication-title: Plant Methods – volume: 72 start-page: 74 year: 2015 end-page: 80 article-title: Managing the evolution of herbicide resistance publication-title: Pest Manag Sci – volume: 64 start-page: 259 year: 2011 end-page: 266 article-title: The role of the growth stage of weeds in their response to reduced herbicide doses publication-title: ACTA Agrobotanica – volume: 8 start-page: 1 year: 2002 end-page: 5 article-title: Four assumptions of multiple regression that researchers should always test publication-title: Pract Assess Res Eval – volume: 182 start-page: 106040 year: 2021 article-title: Application of deep learning to detect lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada publication-title: Comput Electron Agric – volume: 57 start-page: 644 year: 2009 end-page: 651 article-title: Soybean canopy and tillage effects on emergence of Palmeramaranth (Amaranthus palmeri) from a natural seedbank publication-title: Weed Sci – volume: 38 start-page: 341 year: 2021 end-page: 348 article-title: Weed detection in images of carrot fields based on improved YOLO v4 publication-title: Trait Signal – volume: 10 start-page: 372 year: 2021 article-title: A smartphone-based application for scale pest detection using multiple-object detection methods publication-title: Electronics – volume: 58 start-page: 259 year: 2018 end-page: 272 article-title: Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? publication-title: Weed Res – volume: 221 start-page: 430 year: 2019 end-page: 443 article-title: Deep learning based multi-temporal crop classification publication-title: Remote Sens Environ – volume: 10 start-page: 372 year: 2021 ident: S0043174522000534_ref17 article-title: A smartphone-based application for scale pest detection using multiple-object detection methods publication-title: Electronics doi: 10.3390/electronics10040372 – ident: S0043174522000534_ref6 doi: 10.1007/978-981-16-3880-0_35 – ident: S0043174522000534_ref33 doi: 10.1007/978-3-319-54193-8_13 – ident: S0043174522000534_ref48 doi: 10.1017/wet.2020.90 – volume: 67 start-page: 239 year: 2018 ident: S0043174522000534_ref74 article-title: Detection of Carolina geranium (Geranium carolinianum) growing in competition with strawberry using convolutional neural networks publication-title: Weed Sci doi: 10.1017/wsc.2018.66 – volume: 52 start-page: 8448 year: 2022 ident: S0043174522000534_ref84 article-title: RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring publication-title: Appl Intell doi: 10.1007/s10489-021-02893-3 – volume: 64 start-page: 259 year: 2011 ident: S0043174522000534_ref45 article-title: The role of the growth stage of weeds in their response to reduced herbicide doses publication-title: ACTA Agrobotanica doi: 10.5586/aa.2011.068 – volume: 2 start-page: 471 year: 2020 ident: S0043174522000534_ref62 article-title: A deep learning approach for weed detection in lettuce crops using multispectral images publication-title: AgriEngineering doi: 10.3390/agriengineering2030032 – volume: 58 start-page: 259 year: 2018 ident: S0043174522000534_ref24 article-title: Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? publication-title: Weed Res doi: 10.1111/wre.12307 – volume: 1544 start-page: 012033 year: 2020 ident: S0043174522000534_ref21 article-title: Overview of two-stage object detection algorithms publication-title: J Phys Conf Ser doi: 10.1088/1742-6596/1544/1/012033 – ident: S0043174522000534_ref26 – ident: S0043174522000534_ref101 doi: 10.1002/ps.6656 – volume: 44 start-page: 460 year: 2004 ident: S0043174522000534_ref10 article-title: Simulating the effects of weed spatial pattern and resolution of mapping and spraying on economics of site-specific management publication-title: Weed Res doi: 10.1111/j.1365-3180.2004.00423.x – volume: 221 start-page: 430 year: 2019 ident: S0043174522000534_ref100 article-title: Deep learning based multi-temporal crop classification publication-title: Remote Sens Environ doi: 10.1016/j.rse.2018.11.032 – ident: S0043174522000534_ref90 – volume: 11 start-page: 929 year: 1971 ident: S0043174522000534_ref23 article-title: Stage of development descriptions for soybeans, Glycine max (L.) Merrill publication-title: Crop Sci doi: 10.2135/cropsci1971.0011183X001100060051x – ident: S0043174522000534_ref5 – ident: S0043174522000534_ref51 doi: 10.1007/978-3-319-10602-1_48 – ident: S0043174522000534_ref52 – ident: S0043174522000534_ref47 doi: 10.1109/ICCUBEA.2018.8697819 – volume: 53 start-page: 221 year: 2005 ident: S0043174522000534_ref56 article-title: Justification for site-specific weed management based on ecology and economics publication-title: Weed Sci doi: 10.1614/WS-04-071R2 – ident: S0043174522000534_ref12 – ident: S0043174522000534_ref35 doi: 10.1109/CVPR.2017.351 – volume: 78 start-page: 1861 year: 2022 ident: S0043174522000534_ref41 article-title: A novel deep learning-based method for detection of weeds in vegetables publication-title: Pest Manag Sci doi: 10.1002/ps.6804 – ident: S0043174522000534_ref89 – ident: S0043174522000534_ref34 doi: 10.1109/CAIBDA53561.2021.00016 – volume: 13 start-page: 42 year: 2020 ident: S0043174522000534_ref64 article-title: An aerial weed detection system for green onion crops using the you only look once (YOLOv3) deep learning algorithm publication-title: Eng Agric Environ Food doi: 10.37221/eaef.13.2_42 – ident: S0043174522000534_ref28 doi: 10.1016/j.neucom.2017.01.018 – ident: S0043174522000534_ref43 – volume: 35 start-page: 210 year: 2021 ident: S0043174522000534_ref8 article-title: Influence of sulfentrazone and metribuzin applied preemergence on soybean development and yield publication-title: Weed Technol doi: 10.1017/wet.2020.99 – ident: S0043174522000534_ref68 doi: 10.1109/CVPR.2016.91 – ident: S0043174522000534_ref57 – volume-title: R: A Language and Environment for Statistical Computing year: 2021 ident: S0043174522000534_ref67 – ident: S0043174522000534_ref32 doi: 10.1109/CVPR.2016.90 – volume: 72 start-page: 74 year: 2015 ident: S0043174522000534_ref22 article-title: Managing the evolution of herbicide resistance publication-title: Pest Manag Sci doi: 10.1002/ps.4009 – ident: S0043174522000534_ref80 doi: 10.1017/wet.2020.91 – ident: S0043174522000534_ref54 doi: 10.1007/978-3-319-46448-0_2 – volume: 2 start-page: 350 year: 2020 ident: S0043174522000534_ref73 article-title: Mapping and estimating weeds in cotton using unmanned aerial systems-borne imagery publication-title: AgriEngineering doi: 10.3390/agriengineering2020024 – ident: S0043174522000534_ref14 – volume: 11 start-page: 90 year: 2015 ident: S0043174522000534_ref59 article-title: Broad-leaved weeds in chickpea (Cicer arietinum L.) as affected by plant density and Lentagran herbicide application publication-title: Electron J Biol – volume: 38 start-page: 341 year: 2021 ident: S0043174522000534_ref98 article-title: Weed detection in images of carrot fields based on improved YOLO v4 publication-title: Trait Signal doi: 10.18280/ts.380211 – ident: S0043174522000534_ref37 – volume: 10 start-page: 1044 year: 2020 ident: S0043174522000534_ref82 article-title: Spatial modelling of within-field weed populations: a review publication-title: Agronomy doi: 10.3390/agronomy10071044 – ident: S0043174522000534_ref85 doi: 10.1609/aaai.v31i1.11231 – volume: 13 start-page: 54 year: 2021 ident: S0043174522000534_ref11 publication-title: Remote Sens doi: 10.3390/rs13010054 – volume: 5 start-page: 359 year: 2004 ident: S0043174522000534_ref13 article-title: Precision agriculture and sustainability publication-title: Precis Agric doi: 10.1023/B:PRAG.0000040806.39604.aa – ident: S0043174522000534_ref87 – ident: S0043174522000534_ref93 – ident: S0043174522000534_ref7 doi: 10.1109/AVSS.2017.8078518 – volume: 21 start-page: 4350 year: 2021 ident: S0043174522000534_ref95 article-title: Confidence score: the forgotten dimension of object detection performance evaluation publication-title: Sensors doi: 10.3390/s21134350 – ident: S0043174522000534_ref65 – volume: 12 start-page: 4091 year: 2020 ident: S0043174522000534_ref39 article-title: Design and development of a smart variable rate sprayer using deep learning publication-title: Remote Sens doi: 10.3390/rs12244091 – ident: S0043174522000534_ref42 – volume: 20 start-page: 578 year: 2020 ident: S0043174522000534_ref49 article-title: A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network publication-title: Sensors doi: 10.3390/s20030578 – ident: S0043174522000534_ref94 – ident: S0043174522000534_ref9 – ident: S0043174522000534_ref20 doi: 10.1016/j.compag.2019.104963 – ident: S0043174522000534_ref16 – volume: 59 start-page: 357 year: 2019 ident: S0043174522000534_ref53 article-title: In-field classification of herbicide-resistant Papaver rhoeas and Stellaria media using an imaging sensor of the maximum quantum efficiency of photosystem II publication-title: Weed Res doi: 10.1111/wre.12374 – ident: S0043174522000534_ref79 – volume: 184 start-page: 106081 year: 2021 ident: S0043174522000534_ref2 article-title: Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems publication-title: Comput Electron Agric doi: 10.1016/j.compag.2021.106081 – ident: S0043174522000534_ref71 doi: 10.23919/MVA.2017.7986913 – ident: S0043174522000534_ref60 – ident: S0043174522000534_ref4 – volume: 10 start-page: 9548 year: 2020 ident: S0043174522000534_ref75 article-title: Goosegrass detection in strawberry and tomato using a convolutional neural network publication-title: Sci Rep doi: 10.1038/s41598-020-66505-9 – ident: S0043174522000534_ref50 doi: 10.1109/CVPR.2017.211 – ident: S0043174522000534_ref25 – ident: S0043174522000534_ref91 – ident: S0043174522000534_ref3 doi: 10.1109/ICEngTechnol.2017.8308186 – ident: S0043174522000534_ref19 – ident: S0043174522000534_ref36 – ident: S0043174522000534_ref86 – volume: 45 start-page: 364 year: 1997 ident: S0043174522000534_ref15 article-title: The nature and consequence of weed spatial distribution publication-title: Weed Sci doi: 10.1017/S0043174500092997 – ident: S0043174522000534_ref1 doi: 10.1109/ICACCS48705.2020.9074315 – ident: S0043174522000534_ref88 doi: 10.1109/ICCE48956.2021.9352073 – ident: S0043174522000534_ref44 – ident: S0043174522000534_ref96 doi: 10.1109/CSCI46756.2018.00067 – ident: S0043174522000534_ref31 doi: 10.1109/DICTA.2007.4426799 – ident: S0043174522000534_ref78 doi: 10.1186/s40537-019-0197-0 – volume: 16 start-page: 29 year: 2020 ident: S0043174522000534_ref27 article-title: Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields publication-title: Plant Methods doi: 10.1186/s13007-020-00570-z – ident: S0043174522000534_ref77 – ident: S0043174522000534_ref29 doi: 10.1109/ICCV.2015.169 – ident: S0043174522000534_ref55 doi: 10.1109/DLS49591.2019.00011 – ident: S0043174522000534_ref30 doi: 10.1109/CVPR.2014.81 – ident: S0043174522000534_ref18 – volume: 57 start-page: 644 year: 2009 ident: S0043174522000534_ref40 article-title: Soybean canopy and tillage effects on emergence of Palmeramaranth (Amaranthus palmeri) from a natural seedbank publication-title: Weed Sci doi: 10.1614/WS-09-074.1 – ident: S0043174522000534_ref92 – volume: 42 start-page: 523 year: 1994 ident: S0043174522000534_ref46 article-title: Palmer amaranth (Amaranthus palmeri) interference in soybeans (Glycine max). Weed publication-title: Sci – ident: S0043174522000534_ref83 – ident: S0043174522000534_ref58 – ident: S0043174522000534_ref63 doi: 10.1109/LSP.2018.2889273 – volume: 12 start-page: 2136 year: 2020 ident: S0043174522000534_ref81 article-title: Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery publication-title: Remote Sens doi: 10.3390/rs12132136 – volume: 182 start-page: 106040 year: 2021 ident: S0043174522000534_ref38 article-title: Application of deep learning to detect lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada publication-title: Comput Electron Agric doi: 10.1016/j.compag.2021.106040 – ident: S0043174522000534_ref66 – volume: 235 start-page: 142 year: 2019 ident: S0043174522000534_ref97 article-title: Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images publication-title: Field Crop Res doi: 10.1016/j.fcr.2019.02.022 – volume: 68 start-page: 545 year: 2020 ident: S0043174522000534_ref99 article-title: Detection of grassy weeds in bermudagrass with deep convolutional neural networks publication-title: Weed Sci doi: 10.1017/wsc.2020.46 – ident: S0043174522000534_ref69 – volume: 39 start-page: 1137 year: 2017 ident: S0043174522000534_ref70 article-title: Faster R-CNN: towards real-time object detection with region proposal networks publication-title: IEEE T Pattern Anal doi: 10.1109/TPAMI.2016.2577031 – volume: 8 start-page: 1 year: 2002 ident: S0043174522000534_ref61 article-title: Four assumptions of multiple regression that researchers should always test publication-title: Pract Assess Res Eval – volume: 21 start-page: 264 year: 2020 ident: S0043174522000534_ref76 article-title: Vegetation detection and discrimination within vegetable plasticulture row-middles using a convolutional neural network publication-title: Precis Agric doi: 10.1007/s11119-019-09666-6 – ident: S0043174522000534_ref72 |
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| Snippet | Site-specific weed management using open-source object detection algorithms could accurately detect weeds in cropping systems. We investigated the use of... |
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| StartPage | 648 |
| SubjectTerms | Agriculture Algorithms Amaranth Amaranthus palmeri Artificial intelligence Cropping systems Datasets Faster R-CNN Gypsum Herbicides Image annotation Natural populations Neural networks Object recognition Regression analysis Regression models Single Shot Detector site-specific weed management Soybeans TensorFlow Weed control Weeds YOLOv5 |
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| Title | Use of Open-Source Object Detection Algorithms to Detect Palmer Amaranth (Amaranthus palmeri) in Soybean |
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| Volume | 70 |
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