[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116880-en":3,"doc-seo-116880-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116880,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning Methods for Automatic Segmentation of Images of Field-and Glasshouse-Based Plants for High-Throughput Phenotyping","Image segmentation is a critical step in automated high-throughput phenotyping, yet conventional approaches often degrade in complex environments. A fast, robust neural-network-based segmentation tool is developed to phenotype plants in both field and glasshouse settings. Cowpea and wheat images across full growth cycles are converted into multiple color spaces, patched, and pixel-labeled as foreground/background using 24 color-derived properties. After feature selection, multilayer perceptron, SVM, and random forest models are compared with ExG and ExGR, achieving over 98% pixel classification accuracy and strong SPAD prediction.","plants   \nArticle  \nMachine Learning Methods for Automatic Segmentation of Images of Field-and Glasshouse-Based Plants for  \nHigh-Throughput Phenotyping  \nFrank Gyan Okyere 1,2, Daniel Cudjoe 1,2, Pouria Sadeghi-Tehran 1, Nicolas Virlet 1, Andrew B. Riche 1, March Castle 1, Latifa Greche 1, Fady Mohareb 2, Daniel Simms 2, Manal Mhada 3  \nand Malcolm John Hawkesford 1, *  \nCitation: Okyere, F.G.; Cudjoe, D.; Sadeghi-Tehran, P.; Virlet, N.; Riche, A.B.; Castle, M.; Greche, L.; Mohareb, F.; Simms, D.; Mhada, M.; et al. Machine Learning Methods for Automatic Segmentation of Images of Field-and Glasshouse-Based Plants for High-Throughput Phenotyping. Plants 2023, 12, 2035. [https://](https://)[ ](https://)[doi.org/10.3390/plants12102035](doi.org/10.3390/plants12102035)  \nAcademic Editor: Stefano Martellos  \nReceived: 22 March 2023  \nRevised: 3 May 2023  \nAccepted: 10 May 2023  \nPublished: 19 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK  \n2 School of Water, Energy and Environment, Soil, Agrifood and Biosciences, Cran􀀂eld University, Bedford MK43 0AL, UK  \n3 African Integrated Plant and Soil Science, Agro-Biosciences, University of Mohammed VI Polytechnic, Lot 660, Ben Guerir 43150, Morocco  \n* [Correspondence: malcolm.hawkesford@rothamsted.ac.uk](Correspondence: malcolm.hawkesford@rothamsted.ac.uk); Tel.: +44-15-8293-8597  \nAbstract: Image segmentation is a fundamental but critical step for achieving automated highthroughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both 􀀂eld and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from 􀀂eld) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF) . The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green􀂖red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classi􀀂cation accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.  \nKeywords: feature extraction; imaging; machine learning; phenotyping; segmentation  \n1. Introduction  \nThere is an increasing need to improve agriculture to meet the 2050 global food demand agenda. One aspect of this improvement is the implementation of high-throughput plant phenotyping (HTPP) to enable the large-scale evaluation of plant performances","cbCaibcCqxQM8Zqt","https://ap.wps.com/l/cbCaibcCqxQM8Zqt","pdf",3422605,1,22,"English","en",105,"# Introduction\n## High-throughput plant phenotyping and imaging\n## Segmentation as a core processing step\n# Methods (overview)\n## Dataset acquisition in field and glasshouse environments\n## Color space transformation and patch annotation\n## Feature selection and model training\n## Baseline comparisons and evaluation","[{\"question\":\"Why is image segmentation important for high-throughput plant phenotyping?\",\"answer\":\"Segmentation separates plant regions of interest from the background, enabling automated analysis workflows. It is foundational for deriving quantitative phenotyping outputs at scale.\"},{\"question\":\"What environments and plant types are used to evaluate the segmentation method?\",\"answer\":\"The study uses digital images from both glasshouse and field environments, including cowpea (glasshouse) and wheat (field). Images cover different nutrient supplies across their full growth cycle.\"},{\"question\":\"How does the proposed neural-network approach compare with traditional segmentation techniques?\",\"answer\":\"The proposed multilayer perceptron method outperforms other compared methods, producing high-quality segmented images with over 98% pixel classification accuracy. It also yields strong predictive models for SPAD values.\"}]","Machine Learning Methods for Automatic Segmentation of Images of Field-and Glasshouse-Based Plants for High-Throughput Phenotyping | PDF",1785672196,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-methods-for-automatic-segmentation-of-images-of-field-and-glasshouse-based-plants-for-high-throughput-phenotyping","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-methods-for-automatic-segmentation-of-images-of-field-and-glasshouse-based-plants-for-high-throughput-phenotyping/116880/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is image segmentation important for high-throughput plant phenotyping?","Question",{"text":75,"@type":76},"Segmentation separates plant regions of interest from the background, enabling automated analysis workflows. It is foundational for deriving quantitative phenotyping outputs at scale.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What environments and plant types are used to evaluate the segmentation method?",{"text":80,"@type":76},"The study uses digital images from both glasshouse and field environments, including cowpea (glasshouse) and wheat (field). Images cover different nutrient supplies across their full growth cycle.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed neural-network approach compare with traditional segmentation techniques?",{"text":84,"@type":76},"The proposed multilayer perceptron method outperforms other compared methods, producing high-quality segmented images with over 98% pixel classification accuracy. 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