[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120723-en":3,"doc-seo-120723-105":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":20,"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},120723,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine learning in photosynthesis - prospects on sustainable crop development","Improving photosynthesis supports global food security by raising crop yield, yet analyzing the large, complex data generated by photosynthetic research remains a persistent barrier. Machine learning offers richer data analysis across multiple domains of photosynthesis and photosynthetic pigment studies. The review outlines how hyperspectral data can be correlated with photosynthetic parameters using different ML algorithms to enhance yield, and it proposes strategies for applying ML to pigment research to further agricultural productivity.","Review Machine learning in photosynthesis: prospects on sustainable crop development  \nRessin Varghese 1, Aswani Kumar Cherukuri2, Nicholas H Doddrell3, C. George Priya Doss 1, Andrew J. Simkin3,4, Siva Ramamoorthy 1✉  \n1 School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India  \n2 School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India  \n3 School of Biosciences, University of Kent, Canterbury, United Kingdom, CT2 7NJ, UK  \n4 School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK  \n✉ Correspondence addressed to: [siva.ramamoorthy@gmail.com](siva.ramamoorthy@gmail.com) (Siva Ramamoorthy)  \nImproving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.  \nKeywords:  \nPhotosynthesis; Machine learning; Crop yield, Deep learning; Photosynthetic pigments  \nAbbreviations  \nANN- Artificial neural network, CNN, FCN- fully convolutional neural network, DGCNN-Dynamic  \nGraph, ELM-extreme learning machine, CP-ANNs-counter-propagation artificial neural networks, EN  \nEnsemble learning, GBRT-gradient boosting regression tree, GPR-Gaussian process regression, KELM  \nkernel-based extreme learning machine, k-NN- k-nearest neighbors, ML- Machine learning, MLP  \nMultilayer Perceptron, MODIS-Moderate-resolution imaging spectroradiometer, NBC- Naive Bayes  \nClassifier, MLR-Multiple linear regression, NDVI- normalized difference vegetation index, NEE- Net  \necosystem exchange of CO2, PCA-Principle component analysis, PLSR-partial least square regression,  \nQTL-Quantitative trait locus, RBFN-Radial Basis Function Networks, RF-Random Forest, ENET-elastic  \nnet, RFR-random forest regression, RGB-Red, green, blue, SGB-Stochastic gradient boosting, SKNs  \nSupervised Kohonen Networks, SVM- support vector machines , SVR- support vector regression, TE  \nTransposable elements., UAV-Unmanned aerial service, XY-Fs-XY-fused Networks.  \n1. Introduction  \nGlobal food production needs to be increased to feed the growing population by overcoming fluctuating climate changes, decreasing yield productivity, low feed stocks, small rural labor forces, depleted soil fertility, a loss of available agricultural farmland to other uses, reduction in available water resources, and reduced efficacy of agrochemicals [[http://www.fao.org](http://www.fao.org) › wsfs, Zhang et al., 2021). It is obligatory to meet agricultural demands by augmenting crop yield on a global scale. The important role of photosynthesis and photosynthetic pigment research in increasing crop yield has been at the center of many studies (Long et al., 2006). Remarkable outcomes have been reported by modifying the Calvin Benson Cycle (Simkin et al., 2015; López-Calcagno et al., 2020) photorespiration (Simkin et al., 2017b; Lopez-Calcagno et al., 2018), and increasing photosynthetic electron transport rates (López-Calcagno et al., 2020; López-Calcagno et al., 2018) . Strategies to augment photosynthetic rate and biomass with particular reference to photosynthetic pigments have also been investigated in many studies (Simkin et al., 2022). For instance, rice (Oryza sativa) mutants expressing decreased chlorophyll levels formed chloroplast with elevated gene expression of thylakoid membrane proteins. Higher levels of these proteins, whic","cbCaiqXJtbcglL1D","https://ap.wps.com/l/cbCaiqXJtbcglL1D","pdf",1026271,1,51,"English","en",105,"# Introduction\n## Photosynthesis, pigments, and the yield equation\n# Machine learning applications in photosynthetic research\n## Hyperspectral data and photosynthetic parameters","[{\"question\":\"Why is improving photosynthesis important for sustainable crop development?\",\"answer\":\"Improving photosynthesis can increase crop yield and biomass, which helps meet growing food demand under climate-related and agricultural constraints.\"},{\"question\":\"What challenge limits progress in photosynthesis research and yield improvement?\",\"answer\":\"Large datasets produced by many micrometeorological and optical sensing approaches make meaningful analysis time-consuming and difficult.\"},{\"question\":\"How does the review propose machine learning to support crop yield gains?\",\"answer\":\"It reviews ML methods for connecting hyperspectral data with photosynthetic parameters, and it proposes ML-driven strategies to advance photosynthetic pigment research linked to higher yield.\"}]","Machine learning in photosynthesis - 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