Analysis of Color Patterns during the Growth Period of Pleurotus citrinopileatus and Optimization Method for Deep Learning Dataset

Analysis of Color Patterns during the Growth Period of Pleurotus citrinopileatus and Optimization Method for Deep Learning Dataset | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Analysis of Color Patterns during the Growth Period of Pleurotus citrinopileatus and Optimization Method for Deep Learning Dataset Jun Yu, Changshou Luo, Qingfeng Wei, Yang Lu, Yaming Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8983137/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Due to the dependence of deep learning on high-quality datasets and the uncertainty of the Pleurotus citrinopiletus maturity dataset in terms of reproductive period segmentation and image annotation, the study aims to improve the accuracy of dataset annotation by utilizing the color change pattern of Pleurotus citrinopiletus. Firstly, divide the growth period and analyze the color characteristics. Specifically, considering the influence of the cultivation environment, construct a Pleurotus citrinopiletus color characteristic database using data from different environmental batches; The second step is to divide the growth process of Pleurotus citrinopileatus fruiting bodies into four stages: primordium stage, young mushroom stage, fruiting stage, and maturation stage; Step three, color pattern verification: Through HSV color space analysis, it was found that the main hue exhibits periodic changes. Experiments have shown that the mean main color of Pleurotus citrinopileatus during its growth process is not linear, but exhibits periodic fluctuations. The obvious change in the main color usually occurs during the transition from the young mushroom stage to the forming stage and from the forming stage to the mature stage. These color fluctuations may be closely related to the unfolding of the cap, the formation of gills, and the maturation process of spores, confirming the maturity classification. Secondly, feature extraction and model construction. By calculating statistical measures such as the mean, variance, and peak values of the H, S, and V channels for each sample; Then, based on the samples, a gradient boosting tree (GBT) classification model based on color features is constructed; Generate a new dataset based on model validation and adjustment of expert annotations. Thirdly, annotate the experimental results of dataset quality and validate the performance improvement. Under the YOLO12 model, the original dataset and the improved dataset are used for training and testing. The improved dataset is used to train the model with an average accuracy mean（ [email protected] ）Increase by 2.5 percentage points; On the 417 test set, the accuracy of the improved dataset reached 94.5%, while the original dataset was 92%, an increase of 2.5 percentage points. Conclusion: By constructing a color feature database and analyzing the periodic patterns in HSV space, not only has the objective division of reproductive periods been achieved, but also explanatory features with physical significance have been provided for the model, significantly improving the accuracy and reliability of data annotation. Health sciences/Diseases Biological sciences/Physiology Pleurotus citrinopileatus HSV Color Characteristics Maturity Segmentation Feature Extraction Gradient Boosting Tree Deep Learning Dataset Full Text Additional Declarations No competing interests reported. Supplementary Files hebing.rar Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 18 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8983137","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617382017,"identity":"046f7b98-af21-4e20-a3c0-f68e54394653","order_by":0,"name":"Jun Yu","email":"","orcid":"","institution":"Beijing Academy of Agriculture and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yu","suffix":""},{"id":617382018,"identity":"2e3d1812-5540-4a72-9adb-b24cfedfc363","order_by":1,"name":"Changshou 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Color Characteristics, Maturity Segmentation, Feature Extraction, Gradient Boosting Tree, Deep Learning Dataset","lastPublishedDoi":"10.21203/rs.3.rs-8983137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8983137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Due to the dependence of deep learning on high-quality datasets and the uncertainty of the Pleurotus citrinopiletus maturity dataset in terms of reproductive period segmentation and image annotation, the study aims to improve the accuracy of dataset annotation by utilizing the color change pattern of Pleurotus citrinopiletus. Firstly, divide the growth period and analyze the color characteristics. Specifically, considering the influence of the cultivation environment, construct a Pleurotus citrinopiletus color characteristic database using data from different environmental batches; The second step is to divide the growth process of Pleurotus citrinopileatus fruiting bodies into four stages: primordium stage, young mushroom stage, fruiting stage, and maturation stage; Step three, color pattern verification: Through HSV color space analysis, it was found that the main hue exhibits periodic changes. Experiments have shown that the mean main color of Pleurotus citrinopileatus during its growth process is not linear, but exhibits periodic fluctuations. The obvious change in the main color usually occurs during the transition from the young mushroom stage to the forming stage and from the forming stage to the mature stage. These color fluctuations may be closely related to the unfolding of the cap, the formation of gills, and the maturation process of spores, confirming the maturity classification. Secondly, feature extraction and model construction. By calculating statistical measures such as the mean, variance, and peak values of the H, S, and V channels for each sample; Then, based on the samples, a gradient boosting tree (GBT) classification model based on color features is constructed; Generate a new dataset based on model validation and adjustment of expert annotations. Thirdly, annotate the experimental results of dataset quality and validate the performance improvement. Under the YOLO12 model, the original dataset and the improved dataset are used for training and testing. The improved dataset is used to train the model with an average accuracy mean（ [email protected] ）Increase by 2.5 percentage points; On the 417 test set, the accuracy of the improved dataset reached 94.5%, while the original dataset was 92%, an increase of 2.5 percentage points. Conclusion: By constructing a color feature database and analyzing the periodic patterns in HSV space, not only has the objective division of reproductive periods been achieved, but also explanatory features with physical significance have been provided for the model, significantly improving the accuracy and reliability of data annotation.","manuscriptTitle":"Analysis of Color Patterns during the Growth Period of Pleurotus citrinopileatus and Optimization Method for Deep Learning Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 11:21:20","doi":"10.21203/rs.3.rs-8983137/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T18:24:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T08:06:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T13:33:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T09:06:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T08:06:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297619697472987236200032147086731978611","date":"2026-04-03T07:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294409443038648373637810941642596002211","date":"2026-04-02T07:42:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271108856320140996034421232492234826512","date":"2026-04-02T05:22:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17547447380997166838759569365083070597","date":"2026-04-02T00:45:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T00:42:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T19:23:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T06:10:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T04:42:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-19T02:48:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e769c1f-a078-4b69-8dc9-5b2e7ada9a9a","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65703986,"name":"Health sciences/Diseases"},{"id":65703987,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-05-16T15:08:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 11:21:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8983137","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8983137","identity":"rs-8983137","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}