Assessment of wheat chlorophyll content based on an improved whale optimization algorithm
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This study assessed wheat chlorophyll content using an improved whale optimization algorithm applied to leaf color indices extracted from digitized images, achieving an R<sup>2</sup> of 0.77.
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This paper studied how to estimate chlorophyll content in wheat leaves using digitized leaf images by extracting color information from multiple color spaces (RGB, HSI, and L*a*b*) and linking image-derived indices to SPAD chlorophyll meter measurements. The authors used an entropy weighting method to identify image features that correlated with SPAD values and reported that several specific color indices showed strong correlations (R² = 0.745). They then applied an improved whale optimization algorithm (IMWOA) to select/weight color indices for chlorophyll estimation, achieving improved performance (R² = 0.77; RMSE = 2.16). The paper is a Research Square preprint and is not peer reviewed by a journal. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
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Abstract
Abstract The analysis of leaf information derived from digitized leaf images enables the efficient, noninvasive, and real-time estimation of chlorophyll content in a cost-effective manner, facilitating high-throughput assessment. In the present study, leaf color information was captured in various color spaces, such as RGB, HSI and L*a*b*. The entropy weighting method has been employed to estimate the chlorophyll content measured via Soil Plant Analysis Development (SPAD) chlorophyll meter values. The a*, R-B-G, R-G, (a*+b*)/L, a*/b*, (R-G)/(R + G + B), (R-B)/(R + B), H/S and (R-G)/(R + G) exhibited strong correlations (R2 = 0.745) with the SPAD values. Furthermore, the swarm intelligence algorithm, viz. the improved whale optimization algorithm (IMWOA), was applied to assess wheat leaf chlorophyll content by selected image color indices. The experimental results indicate that the IMWOA can achieve the most accurate estimation, obtaining an R2 of 0.77 and a root mean square error (RMSE) of 2.16.
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Assessment of wheat chlorophyll content based on an improved whale optimization algorithm | 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 Assessment of wheat chlorophyll content based on an improved whale optimization algorithm Yufei Song, Xi Meng, Yi Zhou, Yan Li, Zhiguo Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4085635/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 The analysis of leaf information derived from digitized leaf images enables the efficient, noninvasive, and real-time estimation of chlorophyll content in a cost-effective manner, facilitating high-throughput assessment. In the present study, leaf color information was captured in various color spaces, such as RGB, HSI and L*a*b*. The entropy weighting method has been employed to estimate the chlorophyll content measured via Soil Plant Analysis Development (SPAD) chlorophyll meter values. The a*, R-B-G, R-G, (a*+b*)/L, a*/b*, (R-G)/(R + G + B), (R-B)/(R + B), H/S and (R-G)/(R + G) exhibited strong correlations (R 2 = 0.745) with the SPAD values. Furthermore, the swarm intelligence algorithm, viz. the improved whale optimization algorithm (IMWOA), was applied to assess wheat leaf chlorophyll content by selected image color indices. The experimental results indicate that the IMWOA can achieve the most accurate estimation, obtaining an R 2 of 0.77 and a root mean square error (RMSE) of 2.16. Leaf chlorophyll content Digitized leaf images Entropy weighting method Swarm intelligence algorithm Full Text Additional Declarations No competing interests reported. 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. 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