{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T23:00:29Z","timestamp":1769727629072,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T00:00:00Z","timestamp":1738195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03520-x","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T06:40:20Z","timestamp":1738219220000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Performance Evaluation of Modified YOLOv5 Object Detectors for Crop-Weed Classification and Detection in Agriculture Images"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0430-2316","authenticated-orcid":false,"given":"Sandip","family":"Sonawane","sequence":"first","affiliation":[]},{"given":"Nitin N.","family":"Patil","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"3520_CR1","doi-asserted-by":"crossref","unstructured":"Mekonnen G, Mengesha W, Wolde G. Effect of quizalofop-p-ethyl+ bentazone and pendimethalin herbicides on weeds, yield components, and yield of mung bean (vigna radiata (l.) wilczek) in guragie zone, south central ethiopia. International Journal of Agronomy 2024;. Published online","DOI":"10.1155\/2024\/9393014"},{"issue":"29","key":"3520_CR2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.9734\/cjast\/2023\/v42i294203","volume":"42","author":"V Moond","year":"2023","unstructured":"Moond V, Panotra N, Saikanth DRK, Singh G, Prabhavathi N, Verma B. Strategies and technologies in weed management: A comprehensive review. Curr J Appl Sci Technol. 2023;42(29):20\u20139.","journal-title":"Curr J Appl Sci Technol"},{"issue":"6","key":"3520_CR3","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.3390\/agronomy13061595","volume":"13","author":"R Ofosu","year":"2023","unstructured":"Ofosu R, Agyemang ED, M\u00e1rton A, P\u00e1sztor G, Taller J, Kazinczi G. Herbicide resistance: managing weeds in a changing world. Agronomy. 2023;13(6):1595.","journal-title":"Agronomy"},{"issue":"3","key":"3520_CR4","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/drones8030081","volume":"8","author":"S Gokool","year":"2024","unstructured":"Gokool S, Mahomed M, Clulow A, Sibanda M, Kunz R, Naiken V, Mabhaudhi T. Exploring the potential of remote sensing to facilitate integrated weed management in smallholder farms: A scoping review. Drones. 2024;8(3):81.","journal-title":"Drones"},{"key":"3520_CR5","doi-asserted-by":"crossref","unstructured":"Das TK, Behera B, Nath CP, Ghosh S, Sen S, Raj R, Ghosh S, et al. Herbicides use in crop production: An analysis of cost-benefit, non-target toxicities and environmental risks. Crop Protection, 2024;106691","DOI":"10.1016\/j.cropro.2024.106691"},{"issue":"1","key":"3520_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11119-023-10073-1","volume":"25","author":"K Hu","year":"2024","unstructured":"Hu K, Wang Z, Coleman G, Bender A, Yao T, Zeng S, Song D, Schumann A, Walsh M. Deep learning techniques for in-crop weed recognition in large-scale grain production systems: a review. Precision Agriculture. 2024;25(1):1\u201329.","journal-title":"Precision Agriculture"},{"key":"3520_CR7","doi-asserted-by":"publisher","first-page":"07698","DOI":"10.1016\/j.compag.2023.107698","volume":"206","author":"N Rai","year":"2023","unstructured":"Rai N, Zhang Y, Ram BG, Schumacher L, Yellavajjala RK, Bajwa S, Sun X. Applications of deep learning in precision weed management: A review. Comput Electron Agric. 2023;206:07698. https:\/\/doi.org\/10.1016\/j.compag.2023.107698.","journal-title":"Comput Electron Agric"},{"key":"3520_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106081","volume":"184","author":"A Ahmad","year":"2021","unstructured":"Ahmad A, Saraswat D, Aggarwal V, Etienne A, Hancock B. Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems. Comput Electron Agric. 2021;184: 106081. https:\/\/doi.org\/10.1016\/j.compag.2021.106081.","journal-title":"Comput Electron Agric"},{"issue":"11","key":"3520_CR9","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.3390\/s21113647","volume":"21","author":"Z Wu","year":"2021","unstructured":"Wu Z, Chen Y, Zhao B, Kang X, Ding Y. Review of weed detection methods based on computer vision. Sensors. 2021;21(11):3647. https:\/\/doi.org\/10.3390\/s21113647.","journal-title":"Sensors"},{"key":"3520_CR10","doi-asserted-by":"crossref","unstructured":"Attri I, Awasthi LK, Sharma TP, Rathee P. A review of deep learning techniques used in agriculture. Ecological Informatics, 2023;102217","DOI":"10.1016\/j.ecoinf.2023.102217"},{"key":"3520_CR11","doi-asserted-by":"crossref","unstructured":"Kaushik I, Prakash N, Jain A. Plant disease detection using a depth-wise separable-based adaptive deep neural network. Multimedia Tools and Applications, 2024;1\u201329","DOI":"10.1007\/s11042-024-19047-5"},{"key":"3520_CR12","doi-asserted-by":"crossref","unstructured":"Vijayakumar A, Vairavasundaram S. Yolo-based object detection models: A review and its applications. Multimedia Tools and Applications, 2024;1\u201340","DOI":"10.1007\/s11042-024-18872-y"},{"key":"3520_CR13","doi-asserted-by":"crossref","unstructured":"Sapna S, Sandhya S, Shetty RD, Pais SM, Bhattacharjee S. YOLOv5 Model-based Ship Detection in High Resolution SAR Images. In: 2023 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2023;pp. 1\u20136. IEEE","DOI":"10.1109\/CONECCT57959.2023.10234764"},{"key":"3520_CR14","unstructured":"Hassan IU. Weed Detection Using YOLOV5. Medium 2022;. https:\/\/imtiazulhassan.medium.com\/weed-detection-using-yolov5-f860753d8ee8"},{"issue":"12","key":"3520_CR15","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.3390\/agronomy12122953","volume":"12","author":"JM L\u00f3pez-Correa","year":"2022","unstructured":"L\u00f3pez-Correa JM, Moreno H, Ribeiro A, And\u00fajar D. Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops. Agronomy. 2022;12(12):2953. https:\/\/doi.org\/10.3390\/agronomy12122953.","journal-title":"Agronomy"},{"key":"3520_CR16","unstructured":"Medewar A. LabelImg: The Ultimate Tool for Efficient Data Annotation. Medium 2022;. https:\/\/abhishri-medewar.medium.com\/labelimg-the-ultimate-tool-for-efficient-data-annotation-b2fea57fce83"},{"key":"3520_CR17","doi-asserted-by":"crossref","unstructured":"Munaganuri RK, Yamarthi NR. PAMICRM: Improving Precision Agriculture through Multimodal Image Analysis for Crop Water Requirement Estimation Using Multidomain Remote Sensing Data Samples. IEEE Access 2024;.","DOI":"10.1109\/ACCESS.2024.3386552"},{"key":"3520_CR18","volume-title":"Git Code LabelImg","author":"T LabelImg","year":"2015","unstructured":"LabelImg T. Git Code LabelImg. San Francisco, CA, USA: Github; 2015."},{"key":"3520_CR19","doi-asserted-by":"publisher","unstructured":"Bhatti MA, Syam MS, Chen H, Hu Y, Keung LW, Zeeshan Z, Ali YA, Sarhan N. Utilizing Convolutional Neural Networks (CNN) and U-Net Architecture for Precise Crop and Weed Segmentation in Agricultural Imagery: A Deep Learning Approach. Big Data Research, 2024;100465 https:\/\/doi.org\/10.1016\/j.bdr.2024.100465","DOI":"10.1016\/j.bdr.2024.100465"},{"key":"3520_CR20","unstructured":"Tsang S-H. Title of article Brief Review: YOLOv5 for Object Detection. Medium. Last modified June 9, 2023. Accessed March 15, 2023;2024 . https:\/\/sh-tsang.medium.com\/brief-review-yolov5-for-object-detection-84cc6c6a0e3a"},{"issue":"24","key":"3520_CR21","doi-asserted-by":"publisher","first-page":"13074","DOI":"10.3390\/app132413074","volume":"13","author":"C Zhang","year":"2023","unstructured":"Zhang C, Liu J, Li H, Chen H, Xu Z, Ou Z. Weed Detection Method Based on Lightweight and Contextual Information Fusion. Applied Sciences. 2023;13(24):13074. https:\/\/doi.org\/10.3390\/app132413074.","journal-title":"Applied Sciences"},{"key":"3520_CR22","doi-asserted-by":"crossref","unstructured":"Wang C, Liao HM, Wu Y, Chen P, Hsieh J, Yeh I. Cspnet: A new backbone that can enhance learning capability of cnn. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2020;pp. 1571\u20131580","DOI":"10.1109\/CVPRW50498.2020.00203"},{"issue":"7","key":"3520_CR23","doi-asserted-by":"publisher","first-page":"677","DOI":"10.3390\/machines11070677","volume":"11","author":"M Hussain","year":"2023","unstructured":"Hussain M. YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection. Machines. 2023;11(7):677.","journal-title":"Machines"},{"key":"3520_CR24","doi-asserted-by":"crossref","unstructured":"Nepal U, Eslamiat H. Comparing yolov3, yolov4 and yolov5 for autonomous landing spot detection in faulty uavs. Sensors 2022;22(464)","DOI":"10.3390\/s22020464"},{"key":"3520_CR25","doi-asserted-by":"crossref","unstructured":"Xu R, Lin H, Lu K, Cao L, Liu Y. A forest fire detection system based on ensemble learning. Forests 2021;12(217)","DOI":"10.3390\/f12020217"},{"key":"3520_CR26","doi-asserted-by":"publisher","first-page":"5817","DOI":"10.3390\/s22155817","volume":"22","author":"H Liu","year":"2022","unstructured":"Liu H, Sun F, Gu J, Deng L. SF-YOLOv5: A Lightweight Small Object Detection Algorithm Based on Improved Feature Fusion Mode. Sensors. 2022;22:5817. https:\/\/doi.org\/10.3390\/s22155817.","journal-title":"Sensors"},{"key":"3520_CR27","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.1053329","author":"P Wang","year":"2022","unstructured":"Wang P, Tang Y, Luo F, Wang L, Li C, Niu Q, Li H. Weed25: A Deep Learning Dataset for Weed Identification. Front Plant Sci. 2022. https:\/\/doi.org\/10.3389\/fpls.2022.1053329.","journal-title":"Front Plant Sci"},{"issue":"11","key":"3520_CR28","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1055\/s-0038-1675375","volume":"36","author":"T Jaware","year":"2019","unstructured":"Jaware T, Khanchandani K, Zurani A. An accurate automated local similarity factor-based neural tree approach toward tissue segmentation of newborn brain mri. Am J Perinatol. 2019;36(11):1157\u201370. https:\/\/doi.org\/10.1055\/s-0038-1675375.","journal-title":"Am J Perinatol"},{"key":"3520_CR29","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.eja.2019.01.004","volume":"104","author":"J Yu","year":"2019","unstructured":"Yu J, Sharpe SM, Schumann AW, Boyd NS. Deep learning for image-based weed detection in turfgrass. Eur J Agronomy. 2019;104:78\u201384. https:\/\/doi.org\/10.1016\/j.eja.2019.01.004.","journal-title":"Eur J Agronomy"},{"issue":"13","key":"3520_CR30","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.3390\/rs12132136","volume":"12","author":"V Sivakumar","year":"2020","unstructured":"Sivakumar V, Narenthiran A, Li J, Scott S, Psota E, Jhala AJ, Luck JD, Shi Y. Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. Remote Sensing. 2020;12(13):2136. https:\/\/doi.org\/10.3390\/rs12132136.","journal-title":"Remote Sensing"},{"issue":"14","key":"3520_CR31","doi-asserted-by":"publisher","first-page":"8502","DOI":"10.3390\/app13148502","volume":"13","author":"M Sportelli","year":"2023","unstructured":"Sportelli M, Apolo-Apolo OE, Fontanelli M, Frasconi C, Raffaelli M, Peruzzi A, Perez-Ruiz M. Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios. Applied Sciences. 2023;13(14):8502. https:\/\/doi.org\/10.3390\/app13148502.","journal-title":"Applied Sciences"},{"key":"3520_CR32","doi-asserted-by":"publisher","unstructured":"Dang F, Chen D, Lu Y, Li Z. YOLOWeeds: A Novel Benchmark of YOLO Object Detectors for Multi-Class Weed Detection in Cotton Production Systems. Computers and Electronics in Agriculture 205, 2023;107655 https:\/\/doi.org\/10.1016\/j.compag.2023.107655","DOI":"10.1016\/j.compag.2023.107655"},{"key":"3520_CR33","unstructured":"Rahman A, Chen M, Lu Y, Wang H. Weed detection in cotton fields using yolov5: Prototype development and deployment. In: Greer, A.B., C. (eds.) Proceedings of the IISE Annual Conference & Expo 2024 2024;."},{"key":"3520_CR34","doi-asserted-by":"publisher","unstructured":"Wang A, Peng T, Cao H, Xu Y, Wei X, Cui B. Tia-yolov5: An improved yolov5 network for real-time detection of crop and weed in the field. Frontiers in Plant Science 2022;13[SPACE]https:\/\/doi.org\/10.3390\/agriculture12091290","DOI":"10.3390\/agriculture12091290"},{"issue":"9","key":"3520_CR35","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.3390\/agriculture12091290","volume":"12","author":"Y Egi","year":"2022","unstructured":"Egi Y, Hajyzadeh M, Eyceyurt E. Drone-computer communication based tomato generative organ counting model using yolo v5 and deep-sort. Agriculture. 2022;12(9):1290. https:\/\/doi.org\/10.3390\/agriculture12091290.","journal-title":"Agriculture"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03520-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03520-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03520-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T06:40:30Z","timestamp":1738219230000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03520-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,30]]},"references-count":35,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["3520"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03520-x","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,30]]},"assertion":[{"value":"23 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no conflict of interest associated with this research paper. No financial or personal relationships with other people or organizations could have influenced the work or the interpretation of the data presented in this manuscript. All sources of financial support for the research are disclosed. There are no conflicts that might affect the impartiality of the reported findings.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and \/or Animals"}},{"value":"In compliance with ethical standards, all participants involved in this research provided informed consent prior to their inclusion in the study. Participants were provided with detailed information about the study\u2019s purpose, procedures, potential risks, benefits, and their right to withdraw at any point without consequence.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"126"}}