{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:18:24Z","timestamp":1784737104658,"version":"3.55.0"},"reference-count":58,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012431","name":"Jiangsu Provincial Agricultural Science and Technology Independent Innovation Fund","doi-asserted-by":"publisher","award":["CX(21)3184"],"award-info":[{"award-number":["CX(21)3184"]}],"id":[{"id":"10.13039\/501100012431","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32072498"],"award-info":[{"award-number":["32072498"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["scijournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pest Management Science"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>BACKGROUND<\/jats:title><jats:p>Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>A novel neural network, ResNet\u2010101\u2010segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet\u2010101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet\u2010101), ResNet\u2010101\u2010segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (&gt;90%) when trained with sufficient data sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p>ResNet\u2010101\u2010segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet\u2010101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. \u00a9 2024 Society of Chemical Industry.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ps.7979","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T07:29:16Z","timestamp":1706772556000},"page":"2751-2760","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa"],"prefix":"10.1002","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0250-0490","authenticated-orcid":false,"given":"Jie","family":"Yang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University  Nanjing China"},{"name":"Peking University Institute of Advanced Agricultural Sciences\/Shandong Laboratory of Advanced Agricultural Sciences at Weifang  Weifang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Nanjing Forestry University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialin","family":"Yu","sequence":"additional","affiliation":[{"name":"Peking University Institute of Advanced Agricultural Sciences\/Shandong Laboratory of Advanced Agricultural Sciences at Weifang  Weifang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aar8534"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00705-021-05067-1"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2017.05.007"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.2298\/BAH0906465R"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0044-8486(02)00497-0"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2020.128669"},{"key":"e_1_2_8_8_1","first-page":"412","article-title":"Intoxication of cattle by Perilla frutescens (purple mint)","volume":"28","author":"Kerr LA","year":"1986","journal-title":"Vet Human Toxicol"},{"key":"e_1_2_8_9_1","first-page":"1565","article-title":"Screening of weeds and their effect on alfalfa (Medicago sativa)","volume":"87","author":"Mseddi K","year":"2017","journal-title":"Indian J Agric Sci"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0890037X00027895"},{"key":"e_1_2_8_11_1","first-page":"126","article-title":"Evaluation of clethodim for weed control in alfalfa (Medicago sativa L.)","volume":"22","author":"Idris KI","year":"2019","journal-title":"Univ Khartoum J Agric Sci"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1614\/WT-08-082.1"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/ps.2134"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-017-9520-y"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2018.03.001"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1017\/wsc.2020.46"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2011.01.014"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22176581"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e03685"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2017.12.032"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2016.11.021"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.15446\/ing.investig.v37n1.54703"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2869879"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1614\/WT-D-16-00070.1"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_8_26_1","doi-asserted-by":"crossref","unstructured":"SzegedyC LiuW JiaY SermanetP ReedS AnguelovDet al. Going deeper with convolutions. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition; June 7\u201312 2015; Boston MA. IEEE(2015).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_8_27_1","unstructured":"SimonyanKandZissermanA Very deep convolutional networks for large\u2010scale image recognition. InProceedings of the 3rd International Conference on Learning Representations ICLR; May 7\u20139 2015; San Diego CA USA. ICLR(2015)."},{"key":"e_1_2_8_28_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenSandSunJ Deep residual learning for image recognition. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); June 27\u201330 2016; Las Vegas NV USA. IEEE(2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_29_1","doi-asserted-by":"crossref","unstructured":"HuangG LiuZ van derMaatenLandWeinbergerKQ Densely connected convolutional networks. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition; June 21\u201326 2017; Honolulu HI USA. IEEE(2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-020-00529-5"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abf28e"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2021.106623"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci500747n"},{"key":"e_1_2_8_34_1","unstructured":"CiodaroT DevaD De SeixasJandDamazioD Online particle detection with Neural Networks based on topological calorimetry information. InProceedings of the14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT); September 05\u201309 2011; Brunel Univ Uxbridge England. IOP(2012)."},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.053"},{"key":"e_1_2_8_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules27196318"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3206864"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature12346"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114602"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"e_1_2_8_41_1","unstructured":"TompsonJ JainA LeCunYandBreglerC Joint training of a convolutional network and a graphical model for human pose estimation. InProceedings of the 28th Conference on Neural Information Processing Systems; December 08\u201313 2014; Montreal Canada. Neural Information Processing Systems (Nips)(2014)."},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2726546"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.01.009"},{"key":"e_1_2_8_44_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1716999115"},{"key":"e_1_2_8_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2018.07.004"},{"key":"e_1_2_8_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.02.005"},{"key":"e_1_2_8_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-019-09666-6"},{"key":"e_1_2_8_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eja.2019.01.004"},{"key":"e_1_2_8_49_1","doi-asserted-by":"publisher","DOI":"10.1002\/ps.6656"},{"key":"e_1_2_8_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2991354"},{"key":"e_1_2_8_51_1","doi-asserted-by":"publisher","DOI":"10.1017\/wet.2022.46"},{"key":"e_1_2_8_52_1","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12061459"},{"key":"e_1_2_8_53_1","doi-asserted-by":"crossref","unstructured":"MiliotoA LottesPandStachnissC Real\u2010time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. Inthe Proceedings of the IEEE International Conference on Robotics and Automation (ICRA); May 21\u201326 2018; Brisbane Australia. IEEE(2018).","DOI":"10.1109\/ICRA.2018.8460962"},{"key":"e_1_2_8_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2846289"},{"key":"e_1_2_8_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2009.03.002"},{"key":"e_1_2_8_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_25"},{"key":"e_1_2_8_57_1","unstructured":"TaoA BarkerJandSarathyS Detectnet: Deep neural network for object detection in DIGITS(2016).https:\/\/devblogs.nvidia.com\/detectnet-deep-neural-network-object-detection-digits\/[Accessed 2 August 2018]."},{"key":"e_1_2_8_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.03.030"},{"key":"e_1_2_8_59_1","doi-asserted-by":"publisher","DOI":"10.1002\/ps.5349"}],"container-title":["Pest Management Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ps.7979","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:25:43Z","timestamp":1761060343000},"score":1,"resource":{"primary":{"URL":"https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/10.1002\/ps.7979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":58,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["10.1002\/ps.7979"],"URL":"https:\/\/doi.org\/10.1002\/ps.7979","archive":["Portico"],"relation":{},"ISSN":["1526-498X","1526-4998"],"issn-type":[{"value":"1526-498X","type":"print"},{"value":"1526-4998","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"2023-03-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}