Advancing Microbial Activity Inference from Machine Vision Framework Towards Intelligent Control of Environmental Biotechnology for Wastewater Treatment

Advancing Microbial Activity Inference from Machine Vision Framework Towards Intelligent Control of Environmental Biotechnology for Wastewater Treatment | 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 Research Article Advancing Microbial Activity Inference from Machine Vision Framework Towards Intelligent Control of Environmental Biotechnology for Wastewater Treatment Yang Yu, Dongdong Xu, Shujie Zhu, Wenda Chen, Baolan Hu, Ping Zheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6700387/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pioneering biotechnologies hold significant promise for enhancing sustainable wastewater treatment. However, their widespread implementation usually hinges critically on achieving stable and effective control. The emerging deep learning offers a reliable approach to achieve the real-time and accurate monitoring of microbial activity and paving way for the intelligent control of environmental biotechnology. In this context, anaerobic ammonia oxidation (anammox) process, a cutting-edge biotechnology in the past decades, was investigated, and an end-to-end machine vision (MV) framework was developed to directly discern the activity states of sludge based on their distinct visual features presented in thousands of images. The vision model could identify activity states of anammox sludge spanning a wide range with a high accuracy of 84%, and the maximum relative error (RE) of prediction was below 3% on independent tests. This microbial activity prediction performance was further validated in a continuous reactor beyond the model development dataset with RE <5%. Gradient-weighted class activation mapping visualization revealed that autonomous selection of key visual features while ignorance of noise information of microbial aggregates contributed to the superior performance of the vision model for high accuracy recognition of microbial activity. Moving forward, the developed MV framework was applied to predict the microbial activity of anammox sludge sourced from various laboratory and engineering scenarios, and high accuracies of 84.74% and 86.73% were achieved, demonstrating a robust generalization capacity. This study offers a conceptual validation of intelligent identification of microbial activity, and open a new avenue for intelligent control of environmental biotechnology for sustainable wastewater treatment. Environmental Engineering Environmental biotechnology Anammox Microbial activity Intelligent recognition Machine vision framework Mechanism Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6700387","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458838002,"identity":"0a4c97d1-12a9-42a4-b9ab-3b05f6b6a89f","order_by":0,"name":"Yang Yu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yu","suffix":""},{"id":458838003,"identity":"80f13b16-88d1-440e-a821-c6ff9cb2db88","order_by":1,"name":"Dongdong Xu","email":"","orcid":"","institution":"The University of 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However, their widespread implementation usually hinges critically on achieving stable and effective control. The emerging deep learning offers a reliable approach to achieve the real-time and accurate monitoring of microbial activity and paving way for the intelligent control of environmental biotechnology. In this context, anaerobic ammonia oxidation (anammox) process, a cutting-edge biotechnology in the past decades, was investigated, and an end-to-end machine vision (MV) framework was developed to directly discern the activity states of sludge based on their distinct visual features presented in thousands of images. The vision model could identify activity states of anammox sludge spanning a wide range with a high accuracy of 84%, and the maximum relative error (RE) of prediction was below 3% on independent tests. This microbial activity prediction performance was further validated in a continuous reactor beyond the model development dataset with RE \u0026lt;5%. Gradient-weighted class activation mapping visualization revealed that autonomous selection of key visual features while ignorance of noise information of microbial aggregates contributed to the superior performance of the vision model for high accuracy recognition of microbial activity. Moving forward, the developed MV framework was applied to predict the microbial activity of anammox sludge sourced from various laboratory and engineering scenarios, and high accuracies of 84.74% and 86.73% were achieved, demonstrating a robust generalization capacity. 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