{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T13:47:08Z","timestamp":1772200028407,"version":"3.50.1"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Peanut pod rot is one of the major plant diseases affecting peanut production and quality over China, which causes large productivity losses and is challenging to control. To improve the disease resistance of peanuts, breeding is one significant strategy. Crucial preventative and management measures include grading peanut pod rot and screening high-contributed genes that are highly resistant to pod rot should be carried out. A machine vision-based grading approach for individual cases of peanut pod rot was proposed in this study, which avoids time-consuming, labor-intensive, and inaccurate manual categorization and provides dependable technical assistance for breeding studies and peanut pod rot resistance. The Shuffle Attention module has been added to the YOLOv5s (You Only Look Once version 5 small) feature extraction backbone network to overcome occlusion, overlap, and adhesions in complex backgrounds. Additionally, to reduce missing and false identification of peanut pods, the loss function CIoU (Complete Intersection over Union) was replaced with EIoU (Enhanced Intersection over Union). The recognition results can be further improved by introducing grade classification module, which can read the information from the identified RGB images and output data like numbers of non-rotted and rotten peanut pods, the rotten pod rate, and the pod rot grade. The Precision value of the improved YOLOv5s reached 93.8%, which was 7.8%, 8.4%, and 7.3% higher than YOLOv5s, YOLOv8n, and YOLOv8s, respectively; the mAP (mean Average Precision) value was 92.4%, which increased by 6.7%, 7.7%, and 6.5%, respectively. Improved YOLOv5s has an average improvement of 6.26% over YOLOv5s in terms of recognition accuracy: that was 95.7% for non-rotted peanut pods and 90.8% for rotten peanut pods. This article presented a machine vision- based grade classification method for peanut pod rot, which offered technological guidance for selecting high-quality cultivars with high resistance to pod rot in peanut.<\/jats:p>","DOI":"10.3389\/fpls.2024.1364185","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T10:59:10Z","timestamp":1713178750000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Classification of peanut pod rot based on improved YOLOv5s"],"prefix":"10.3389","volume":"15","author":[{"given":"Yu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiukun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifeng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Shao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhong","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"445","DOI":"10.21608\/jpp.2013.72146","article-title":"Inheritance of some economic characters, reaction to pod rot diseases and aflatoxen contamination in peanut (arachis hypogaea l.)","volume":"4","author":"Abd El-aal","year":"2013","journal-title":"J. Plant Product."},{"key":"B2","first-page":"1","article-title":"Automated cashew kernel grading using machine vision","author":"Aran","year":"2016"},{"key":"B3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3146\/pnut.30.1.0010","article-title":"Assessment of six peanut cultivars for control of rhizoctonia pod rot when sprayed with azoxystrobin or tebuconazole1","volume":"30","author":"Besler","year":"2003","journal-title":"Peanut Sci."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1094\/PDIS-07-15-0840-PDN","article-title":"First report of rhizoctonia solani causing peanut pod rot in China","volume":"100","author":"Chi","year":"2015","journal-title":"Plant Dis."},{"key":"B5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s005720050284","article-title":"Influence of the endomycorrhizal fungus glomus mosseae on the development of peanut pod rot disease in Egypt","volume":"10","author":"Elsayed Abdalla","year":"2000","journal-title":"Mycorrhiza"},{"key":"B6","doi-asserted-by":"publisher","first-page":"13895","DOI":"10.1007\/s00521-021-06029-z","article-title":"A detection algorithm for cherry fruits based on the improved yolo-v4 model","volume":"35","author":"Gai","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.1094\/PDIS-06-11-0489","article-title":"First report of peanut foot rot caused by neocosmospora vasinfecta var. Africana in jiangxi province, China","volume":"95","author":"Gai","year":"2011","journal-title":"Plant Dis."},{"key":"B8","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.jksuci.2018.06.006","article-title":"Machine vision based papaya disease recognition","volume":"32","author":"Habib","year":"2020","journal-title":"J. King Saud Univ. - Comput. Inf. Sci."},{"key":"B9","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.gltp.2022.03.016","article-title":"Plant leaf disease detection using computer vision and machine learning algorithms","volume":"3","author":"Harakannanavar","year":"2022","journal-title":"Global Trans. Proc."},{"key":"B10","doi-asserted-by":"publisher","first-page":"493","DOI":"10.13926\/j.cnki.apps.000494","article-title":"Fusarium neocosmosporiellum causing peanut pod rot and its biological characteristics","volume":"52","author":"He","year":"2022","journal-title":"Acta Phytopathol. Sin."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1007\/s13369-022-06851-0","article-title":"Mfaster r-cnn for maize leaf diseases detection based on machine vision","volume":"48","author":"He","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"B12","doi-asserted-by":"publisher","first-page":"9471","DOI":"10.1007\/s00521-021-06388-7","article-title":"Multi-feature decision fusion algorithm for disease detection on crop surface based on machine vision","volume":"34","author":"Hua","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1038\/s41598-021-81216-5","article-title":"Tomato detection based on modified yolov3 framework","volume":"11","author":"Lawal","year":"2021","journal-title":"Sci. Rep."},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0282297","article-title":"Yolov5-LiNet: A lightweight network for fruits instance segmentation","volume":"18","author":"Lawal","year":"2023","journal-title":"PloS One"},{"key":"B15","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3146\/i0095-3679-17-2-11","article-title":"Reaction of peanut cultivars to pythium pod rot and their influence on populations of pythium spp. In soil1","volume":"17","author":"Lewis","year":"1990","journal-title":"Peanut Sci."},{"key":"B16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.16318\/j.cnki.hbnykx.2011.05.012","article-title":"Identification and pathogenicity of peanut pod rot in hebei province, China","volume":"15","author":"Li","year":"2011","journal-title":"J. Hebei Agric. Sci."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single shot multibox detector","volume":"9905","author":"Liu","year":"2016","journal-title":"Lecture Notes in Computer Science"},{"key":"B18","doi-asserted-by":"publisher","first-page":"104","DOI":"10.15889\/j.issn.1002-1302.2020.06.021","article-title":"Isolation, characterization and biological properties of peanut pod rot bacteria in hainan province","volume":"48","author":"Liu","year":"2020","journal-title":"Jiangsu Agric. Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"123","DOI":"10.11975\/j.issn.1002-6819.2022.22.013","article-title":"Fast identification of nematodes based on coordinate attention mechanism and efficient bounding box regression loss","volume":"38","author":"Lu","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"B20","doi-asserted-by":"publisher","first-page":"2007","DOI":"10.13031\/2013.19994","article-title":"Statistical and neural network classifiers for citrus disease detection using machine vision","volume":"48","author":"Pydipati","year":"2005","journal-title":"Trans. ASAE"},{"key":"B21","first-page":"10208","article-title":"DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution","author":"Qiao","year":"2021"},{"key":"B22","first-page":"779","article-title":"You only look once: unified, real-time object detection","author":"Redmon","year":"2016"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. On Pattern Anal. Mach. Intell."},{"key":"B24","first-page":"171","article-title":"Improved lightweight peanut detection algorithm based on yolo v3","author":"Shen","year":"2021"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12020319","article-title":"Automatic bunch detection in white grape varieties using yolov3, yolov4, and yolov5 deep learning algorithms","volume":"12","author":"Sozzi","year":"2022","journal-title":"Agronomy"},{"key":"B26","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1094\/PDIS-03-11-0240","article-title":"First report of peanut pod rot caused by neocosmospora vasinfecta in northern China","volume":"96","author":"Sun","year":"2012","journal-title":"Plant Dis."},{"key":"B27","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1094\/PDIS-06-11-0461","article-title":"First report of neocosmospora striata causing peanut pod rot in China","volume":"96","author":"Sun","year":"2011","journal-title":"Plant Dis."},{"key":"B28","doi-asserted-by":"publisher","first-page":"109","DOI":"10.3146\/i0095-3679-7-2-13","article-title":"Effect of gypsum on yield, grade and incidence of pod rot in five peanut cultivars1","volume":"7","author":"Walker","year":"1980","journal-title":"Peanut Sci."},{"key":"B29","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.cropro.2016.04.011","article-title":"Management of peanut pod rot ii: comparison of calendar and threshold-based fungicide timings","volume":"87","author":"Wheeler","year":"2016","journal-title":"Crop Prot."},{"key":"B30","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1146\/annurev.py.29.090191.001431","article-title":"Breeding for disease resistance in peanut (arachis hypogaea l.)","volume":"29","author":"Wynne","year":"1991","journal-title":"Annu. Rev. Phytopathol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.1094\/PDIS-02-19-0321-PDN","article-title":"First report of pythium myriotylum associated with peanut pod rot in China","volume":"103","author":"Yu","year":"2019","journal-title":"Plant Dis."},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2022.103514","article-title":"A survey of modern deep learning based object detection models","volume":"126","author":"Zaidi","year":"2022","journal-title":"Digit. Signal Process."},{"key":"B33","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and efficient iou loss for accurate bounding box regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"B34","article-title":"SA-net: shuffle attention for deep convolutional neural networks","author":"Zhang","year":"2021","journal-title":"Arxiv"},{"key":"B35","doi-asserted-by":"publisher","first-page":"27","DOI":"10.14001\/j.issn.1002-4093.2016.03.005","article-title":"Identification and biological characteristics of fungal isolates causing peanut pod rot pathogen","volume":"45","author":"Zhang","year":"2016","journal-title":"Peanut J."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2024.1364185\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T10:59:22Z","timestamp":1713178762000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2024.1364185\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,15]]},"references-count":35,"alternative-id":["10.3389\/fpls.2024.1364185"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2024.1364185","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,15]]},"article-number":"1364185"}}