{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T22:27:58Z","timestamp":1784154478024,"version":"3.55.0"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013061","name":"Jilin Scientific and Technological Development Program","doi-asserted-by":"publisher","award":["20210203211SF"],"award-info":[{"award-number":["20210203211SF"]}],"id":[{"id":"10.13039\/501100013061","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013061","name":"Jilin Scientific and Technological Development Program","doi-asserted-by":"publisher","award":["20220202036NC"],"award-info":[{"award-number":["20220202036NC"]}],"id":[{"id":"10.13039\/501100013061","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010211","name":"Education Department of Jilin Province","doi-asserted-by":"publisher","award":["JJKH20210335KJ"],"award-info":[{"award-number":["JJKH20210335KJ"]}],"id":[{"id":"10.13039\/501100010211","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Spatial Pyramid Pooling (SPP) is important in capturing remote contextual information for pixel-level prediction tasks in scene-resolved detection of rice diseases. In this paper, the detection objects of the rice disease dataset used in this paper have almost the same target size and do not need to be passed through different filters to obtain different receptive fields of view. Therefore, this paper proposed a new pooling structure, SPPFCSPC-G, which split the feature vector into 2 channels for processing. One channel was processed using grouped 1\u00d71 Conv, while the other channel mainly used multiple filters with the same parallel structure (5\u00d75 MaxPool). Additionally, multiple 1\u00d71 and 3\u00d73 grouped convolutions were concatenated in series in that branch (Group-Conv) to extract more complex features in rice. Finally, the 2 parts were connected (Concat) together, with each convolutional layer Conv divided into 4 groups as a way to reduce the amount of computation in the model. The project team incorporated SPPFCSPC-G into the Backbone of YOLOv5 and trained it on NVIDIA Tesla T4 (GPU). The experimental results showed that the performance of the method used in this paper improved, including Precision, Recall, mAP, and training speed, while reducing the size of computational parameters (Parameters), computational volume (GFLOPs), and model size (Param.). The project team carried out the trained YOLOv5 model on Intel Core i5 (CPU) for inference detection of rice leaves in real scenarios, and the experiments showed that both pre-inference and actual inference were faster. Moreover, the consumption of computational resources was almost minimized, and the model effectively identified rice diseases.<\/jats:p>","DOI":"10.1371\/journal.pone.0295661","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T18:26:15Z","timestamp":1702664775000},"page":"e0295661","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Research on rice disease recognition based on improved SPPFCSPC-G YOLOv5 network"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9650-8195","authenticated-orcid":true,"given":"Bo","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4093-1171","authenticated-orcid":true,"given":"Lina","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinping","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"issue":"01","key":"pone.0295661.ref001","first-page":"91","article-title":"Evaluation of resistance of new rice varieties (lines) and main varieties to rice blast in Jiangsu Province during 2016\u20132020","volume":"50","author":"Qi Zhongqiang","year":"2020","journal-title":"Jiangsu Agricultural Sciences"},{"issue":"21","key":"pone.0295661.ref002","first-page":"100","article-title":"Isolation and antagonism of endophytic bacteria from rice bacterial blight","volume":"45","author":"Yang Junyu","year":"2017","journal-title":"Jiangsu agricultural sciences"},{"key":"pone.0295661.ref003","volume-title":"International comparison and empirical analysis on the research level of three main rice diseases based on bibliometrics","author":"Ding Lin","year":"2013"},{"key":"pone.0295661.ref004","volume-title":"Research and Implementation of Rice Leaf Disease Detection Based on Deep Learning","author":"DaPeng Song","year":"2022"},{"issue":"11","key":"pone.0295661.ref005","first-page":"3570","article-title":"Application of improved LeNet-5 model in apple image recognition","volume":"39","author":"Li-chao ZHANG","year":"2018","journal-title":"Computer Engineering and Design"},{"issue":"17","key":"pone.0295661.ref006","first-page":"205","article-title":"Orange recognition method using improved YOLOv3-LITE lightweight neural network","volume":"35","author":"Lv Shilei","year":"2019","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"issue":"4","key":"pone.0295661.ref007","first-page":"92","article-title":"Study on the detection of broccoli leaves based on the improved SSD network","volume":"41","author":"Fu Zhongzheng","year":"2020","journal-title":"Journal of Chinese Agricultural Mechanization"},{"issue":"12","key":"pone.0295661.ref008","first-page":"175","article-title":"Recognition of plant leaf diseases,insect,and pests based on improved ResNet","volume":"42","author":"Cao Yueteng","year":"2021","journal-title":"Journal of Chinese Agricultural Mechanization"},{"issue":"12","key":"pone.0295661.ref009","first-page":"171","article-title":"Weed identification algorithm of weeding robot based on Faster R-CNN [J]","volume":"40","author":"Li Chunming","year":"2019","journal-title":"Journal of Chinese Agricultural Mechanization"},{"issue":"4","key":"pone.0295661.ref010","first-page":"1","article-title":"Potato Detection and Segmentation Based on Mask R-CNN","volume":"45","author":"H S Lee","year":"2020","journal-title":"Journal of Biosystems Engineering"},{"issue":"3","key":"pone.0295661.ref011","first-page":"156","article-title":"Recognition of apple targets before fruits thinning by robot based on R-FCN deep convolution neural network","volume":"35","author":"Wang Dandan","year":"2019","journal-title":"Transactions of the Chinese Society of Agricultural Engineering"},{"key":"pone.0295661.ref012","doi-asserted-by":"crossref","unstructured":"Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi. 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