{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:36:46Z","timestamp":1792809406175,"version":"4.5.0"},"reference-count":101,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop\u2019s genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R<jats:sup>2 <\/jats:sup>= 0.93), while XGBoost was also reasonably effective (R<jats:sup>2 <\/jats:sup>= 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.<\/jats:p>","DOI":"10.3389\/fpls.2024.1339864","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T04:29:37Z","timestamp":1708403377000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms"],"prefix":"10.3389","volume":"15","author":[{"given":"N. Ace","family":"Pugh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Young","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manisha","family":"Ojha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yves","family":"Emendack","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacobo","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanguo","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveen","family":"Puppala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"344","DOI":"10.3390\/agronomy9070344","article-title":"Estimating biomass of black oat using UAV-based RGB imaging","volume":"9","author":"Acorsi","year":"2019","journal-title":"Agronomy"},{"key":"B2","doi-asserted-by":"publisher","first-page":"754","DOI":"10.4236\/ojs.2015.57075","article-title":"Variance inflation factor: as a condition for the inclusion of suppressor variable (s) in regression analysis","volume":"5","author":"Akinwande","year":"2015","journal-title":"Open J. Stat"},{"key":"B3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.tplants.2018.02.001","article-title":"Translating high-throughput phenotyping into genetic gain","volume":"23","author":"Araus","year":"2018","journal-title":"Trends Plant Sci."},{"key":"B4","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.isprsjprs.2020.09.015","article-title":"Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data","volume":"169","author":"Ashapure","year":"2020","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"B5","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/S0378-4290(02)00216-2","article-title":"Controlling canopy formation, flowering, and yield in field-grown stands of peanut (Arachis hypogaea L.) with ambient and regulated soil temperature","volume":"81","author":"Awal","year":"2003","journal-title":"Field Crops Res."},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1002\/ppj2.20081","article-title":"Phenotyping agronomic and physiological traits in peanut under mid-season drought stress using UAV-based hyperspectral imaging and machine learning","volume":"6","author":"Bagherian","year":"2023","journal-title":"Plant Phenome J."},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09569-8","article-title":"Emerging trends in machine learning to predict crop yield and study its influential factors: A survey","author":"Bali","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"B8","first-page":"54","article-title":"Exploratory use of a UAV platform for variety selection in peanut","volume-title":"Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping","author":"Balota","year":"2016"},{"key":"B9","first-page":"91","article-title":"matplotlib\u2013A Portable Python Plotting Package","volume-title":"Astronomical data analysis software and systems XIV","author":"Barrett","year":"2005"},{"key":"B10","first-page":"875","article-title":"Grid search in hyperparameter optimization of machine learning models for prediction of HIV\/AIDS test results","volume":"44","author":"Belete","year":"2022","journal-title":"Int. J. Comput. Appl."},{"key":"B11","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. photogram. Remote Sens."},{"key":"B12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3146\/i0095-3679-9-1-11","article-title":"Growth stages of peanut (Arachis hypogaea L.)","volume":"9","author":"Boote","year":"1982","journal-title":"Peanut Sci."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.3390\/rs12101644","article-title":"Closing the phenotyping gap: High resolution UAV time series for soybean growth analysis provides objective data from field trials","volume":"12","author":"Borra-Serrano","year":"2020","journal-title":"Remote Sens."},{"key":"B14","first-page":"120","article-title":"The openCV library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s Journal: Softw. Tools Prof. Program."},{"key":"B15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2019.01491","article-title":"Combining crop growth modeling and statistical genetic modeling to evaluate phenotyping strategies","volume":"10","author":"Bustos-Korts","year":"2019","journal-title":"Front. Plant Sci."},{"key":"B17","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1590\/1809-4430-eng.agric.v39nep33-40\/2019","article-title":"Correlations among vegetation indices and peanut traits during different crop development stages","volume":"39","author":"Carneiro","year":"2019","journal-title":"Engenharia Agr\u00edcola"},{"key":"B18","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3146\/0095-3679-46.1A.91","article-title":"Not your grandma\u2019s goobers: designing the future of peanut breeding","volume":"46","author":"Chamberlin","year":"2019","journal-title":"Peanut Sci."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5723567","article-title":"Unmanned aircraft system-(UAS-) based high-throughput phenotyping (HTP) for tomato yield estimation","volume":"2021","author":"Chang","year":"2021","journal-title":"J. Sensors"},{"key":"B20","first-page":"785","article-title":"Xgboost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"B21","first-page":"1","article-title":"Xgboost: extreme gradient boosting","volume":"1","author":"Chen","year":"2015","journal-title":"R Package version 0.4-2"},{"key":"B22","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1002\/9781119874157.ch10","article-title":"Role of the genomics\u2013phenomics\u2013agronomy paradigm in plant breeding","volume":"46","author":"Chen","year":"2022","journal-title":"Plant Breed. Rev."},{"key":"B23","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1080\/07038992.2020.1833186","article-title":"Autumn crop yield prediction using data-driven approaches: -support vector machines, random forest, and deep neural network methods","volume":"47","author":"Dang","year":"2021","journal-title":"Can. J. Remote Sens."},{"key":"B24","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/B978-1-63067-038-2.00011-3","article-title":"Peanut composition, flavor and nutrition","author":"Davis","year":"2016","journal-title":"Peanuts: Gene. Process. Util."},{"key":"B25","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.1007\/s00521-022-07856-4","article-title":"An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost","volume":"35","author":"Demir","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"B26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/bs.agron.2020.02.002","article-title":"Advanced analytics, phenomics and biotechnology approaches to enhance genetic gains in plant breeding","volume":"162","author":"Dwivedi","year":"2020","journal-title":"Adv. Agron."},{"key":"B27","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/J.FCR.2018.01.034","article-title":"Potential impact of climate change on peanut yield in Senegal, West Africa","volume":"219","author":"Faye","year":"2018","journal-title":"Field Crops Res."},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1002\/ppj2.20023","article-title":"Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants","volume":"4","author":"Feldmann","year":"2021","journal-title":"Plant Phenome J."},{"key":"B29","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/B978-1-63067-038-2.00010-1","article-title":"An overview of world peanut markets","author":"Fletcher","year":"2016","journal-title":"Peanuts"},{"key":"B30","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.tplants.2011.09.005","article-title":"Phenomics\u2013technologies to relieve the phenotyping bottleneck","volume":"16","author":"Furbank","year":"2011","journal-title":"Trends Plant Sci."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2135\/tppj2019.07.0011","article-title":"In-field whole-plant maize architecture characterized by subcanopy rovers and latent space phenotyping","volume":"2","author":"Gage","year":"2019","journal-title":"Plant Phenome J."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.3390\/rs14041023","article-title":"Prediction of soil water content and electrical conductivity using random forest methods with UAV multispectral and ground-coupled geophysical data","volume":"14","author":"Guan","year":"2022","journal-title":"Remote Sens."},{"key":"B33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10095020.2022.2155255","article-title":"Modelling future\u00a0spatial distribution of peanut crops in Australia under climate change scenarios","author":"Haerani","year":"2023","journal-title":"Geo-spatialInform. Sci."},{"key":"B34","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3102\/1076998619832248","article-title":"Machine learning made easy: a review of scikit-learn package in python programming language","volume":"44","author":"Hao","year":"2019","journal-title":"J. Educ. Behav. Stat"},{"key":"B35","doi-asserted-by":"publisher","first-page":"543181","DOI":"10.1101\/543181","article-title":"Using extreme gradient boosting (XGBoost) to evaluate the importance of a suite of environmental variables and to predict recruitment of young-of-the-year spotted seatrout in Florida","author":"Herdter Smith","year":"2019","journal-title":"BioRxiv"},{"key":"B36","doi-asserted-by":"publisher","first-page":"107609","DOI":"10.1016\/j.agrformet.2019.06.008","article-title":"Assimilation of remote sensing into crop growth models: Current status and perspectives","volume":"276","author":"Huang","year":"2019","journal-title":"Agric. For. meteorol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"5085","DOI":"10.1093\/jxb\/erac286","article-title":"Plant phenotyping for a sustainable future","volume":"73","author":"Janni","year":"2022","journal-title":"J. Exp. Bot."},{"key":"B38","doi-asserted-by":"publisher","first-page":"102194","DOI":"10.1016\/j.ecoinf.2023.102194","article-title":"Winter wheat yield prediction in the conterminous United States using solar induced chlorophyll fluorescence data and XGBoost and random forest algorithm","volume":"77","author":"Joshi","year":"2023","journal-title":"Ecol. Inf."},{"key":"B39","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.3390\/rs14122843","article-title":"A geographically weighted random forest approach to predict corn yield in the US corn belt","volume":"14","author":"Khan","year":"2022","journal-title":"Remote Sens."},{"key":"B40","doi-asserted-by":"publisher","first-page":"558","DOI":"10.4097\/kja.19087","article-title":"Multicollinearity and misleading statistical results","volume":"72","author":"Kim","year":"2019","journal-title":"Korean J. anesthesiol."},{"key":"B41","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1093\/ajcn\/70.6.1009","article-title":"High\u2013monounsaturated fatty acid diets lower both plasma cholesterol and triacylglycerol concentrations","volume":"70","author":"Kris-Etherton","year":"1999","journal-title":"Am. J. Clin. Nutr."},{"key":"B42","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.proeng.2015.12.390","article-title":"Application of logistic growth curve","volume":"131","author":"Kucharavy","year":"2015","journal-title":"Proc. Eng."},{"key":"B43","doi-asserted-by":"publisher","first-page":"3301","DOI":"10.1002\/csc2.20514","article-title":"High throughput can produce better decisions than high accuracy when phenotyping plant populations","volume":"61","author":"Lane","year":"2021","journal-title":"Crop Sci."},{"key":"B44","doi-asserted-by":"crossref","first-page":"108592","DOI":"10.1016\/j.agee.2023.108592","article-title":"Elevated [CO2] enhances soil respiration and AMF abundance in a semiarid peanut agroecosystem","volume":"355","author":"Laza","year":"2023","journal-title":"Agriculture Ecosyst. Environ."},{"key":"B45","doi-asserted-by":"crossref","first-page":"108599","DOI":"10.1016\/j.agrformet.2021.108599","article-title":"Effect of elevated CO2 on peanut performance in a semi-arid production region","volume":"308","author":"Laza","year":"2021","journal-title":"Agric. For. Meteorology"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.5772\/intechopen.90672","article-title":"Factors affecting yield of crops","volume":"9","author":"Liliane","year":"2020","journal-title":"Agronomy-climate Change Food Secur."},{"key":"B47","doi-asserted-by":"publisher","first-page":"108698","DOI":"10.1016\/j.agrformet.2021.108698","article-title":"Machine learning in crop yield modelling: A powerful tool, but no surrogate for science","volume":"312","author":"Lischeid","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"B48","first-page":"246","article-title":"New machine learning algorithm: Random forest","author":"Liu","year":"2012"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2017.02181","article-title":"Plant phenotyping and phenomics for plant breeding","volume":"8","author":"Lobos","year":"2017","journal-title":"Front. Plant Sci."},{"key":"B50","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.biosystemseng.2020.11.010","article-title":"Combining plant height, canopy coverage and vegetation index from UAV-based RGB images to estimate leaf nitrogen concentration of summer maize","volume":"202","author":"Lu","year":"2021","journal-title":"Biosyst. Eng."},{"key":"B51","doi-asserted-by":"publisher","first-page":"415","DOI":"10.4236\/ajps.2023.143027","article-title":"Use of unmanned aerial system (UAS) phenotyping to predict pod and seed yield in organic peanuts","volume":"14","author":"Manley","year":"2023","journal-title":"Am. J. Plant Sci."},{"key":"B52","doi-asserted-by":"publisher","first-page":"808","DOI":"10.3390\/rs70100808","article-title":"Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing","volume":"7","author":"Marshall","year":"2015","journal-title":"Remote Sens."},{"key":"B53","first-page":"1","article-title":"pandas: a foundational Python library for data analysis and statistics","volume":"14","author":"McKinney","year":"2011","journal-title":"Python High Perform. Sci. comput."},{"key":"B54","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/S004250050620\/METRICS","article-title":"Auxin redistributes upwards in graviresponding gynophores of the peanut plant","volume":"209","author":"Moctezuma","year":"1999","journal-title":"Planta"},{"key":"B55","first-page":"407","volume-title":"A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis","author":"Nazari","year":"2023"},{"key":"B56","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.agrformet.2013.09.007","article-title":"Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras","volume":"184","author":"Nijland","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"B57","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1094\/PDIS-07-21-1555-RE","article-title":"Two new bacterial pathogens of peanut, causing early seedling decline disease, identified in the Texas Panhandle","volume":"106","author":"Obasa","year":"2022","journal-title":"Plant Dis."},{"key":"B58","doi-asserted-by":"publisher","first-page":"394","DOI":"10.37398\/JSR.2020.640254","article-title":"Performance evaluation of machine learning techniques for mustard crop yield prediction from soil analysis","volume":"64","author":"Pandith","year":"2020","journal-title":"J. Sci. Res."},{"key":"B59","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1534\/g3.115.023515","article-title":"Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton","volume":"6","author":"Pauli","year":"2016","journal-title":"G3:\u00a0Genes Genomes Genet."},{"key":"B60","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"B61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2135\/tppj2017.08.0006","article-title":"Temporal estimates of crop growth in sorghum and maize breeding enabled by unmanned aerial systems","volume":"1","author":"Pugh","year":"2018","journal-title":"Plant Phenome J."},{"key":"B62","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1080\/15427528.2019.1627686","article-title":"A statistical evaluation of replicated block designs and spatial variability in sorghum performance trials","volume":"33","author":"Pugh","year":"2019","journal-title":"J. Crop Improve."},{"key":"B63","doi-asserted-by":"publisher","first-page":"e20026","DOI":"10.1002\/ppj2.20026","article-title":"Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems","volume":"4","author":"Pugh","year":"2021","journal-title":"Plant Phenome J."},{"key":"B64","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2023.1121462","article-title":"Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance","volume":"14","author":"Puppala","year":"2023","journal-title":"Front. Genet."},{"key":"B65","volume-title":"QGIS Geographic Information System","year":"2023"},{"key":"B66","doi-asserted-by":"publisher","first-page":"6732","DOI":"10.3390\/s20236732","article-title":"Estimation of peanut leaf area index from unmanned aerial vehicle multispectral images","volume":"20","author":"Qi","year":"2020","journal-title":"Sensors"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.3390\/rs15174286","article-title":"Estimation of soybean yield by combining maturity group information and unmanned aerial vehicle multi-sensor data using machine learning","volume":"15","author":"Ren","year":"2023","journal-title":"Remote Sensing."},{"key":"B68","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1023\/B:VLSI.0000028532.53893.82","article-title":"Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement","volume":"38","author":"Reza","year":"2004","journal-title":"J. VLSI Signal Process. Syst. signal image video Technol."},{"key":"B69","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ"},{"key":"B70","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.beproc.2018.01.004","article-title":"Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behavior of cattle","volume":"148","author":"Ruuska","year":"2018","journal-title":"Behav. processes"},{"key":"B71","doi-asserted-by":"publisher","first-page":"5402","DOI":"10.1080\/01431161.2018.1448484","article-title":"The potential for RGB images obtained using unmanned aerial vehicle to assess and predict yield in sugarcane fields","volume":"39","author":"Sanches","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"B72","doi-asserted-by":"publisher","DOI":"10.1002\/ppj2.20003","article-title":"High-throughput measurement of peanut canopy height using digital surface models","volume":"3","author":"Sarkar","year":"2020","journal-title":"Plant Phenome J."},{"key":"B73","first-page":"10","article-title":"Statsmodels: Econometric and statistical modeling with python","author":"Seabold","year":"2010"},{"key":"B74","first-page":"3","article-title":"Peanuts and their nutritional aspects\u2014a review","author":"Settaluri","year":"2012","journal-title":"Food Nutr. Sci."},{"key":"B75","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.3934\/era.2023169","article-title":"Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach","volume":"31","author":"Shahi","year":"2023","journal-title":"Electron. Res. Arch."},{"key":"B76","doi-asserted-by":"publisher","first-page":"107124","DOI":"10.1016\/j.ecolind.2020.107124","article-title":"Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling","volume":"121","author":"Shammi","year":"2021","journal-title":"Ecol. Indic."},{"key":"B77","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.compag.2018.04.023","article-title":"Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection","volume":"150","author":"Sharif","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"B78","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0159781","article-title":"Unmanned aerial vehicles for high-throughput phenotyping and agronomic research","volume":"11","author":"Shi","year":"2016","journal-title":"PloS One"},{"key":"B79","first-page":"1","article-title":"Rice growing stage monitoring in small-scale region using ExG vegetation index","author":"Soontranon","year":"2014"},{"key":"B80","first-page":"129","article-title":"Analysis of spatial factors influencing crop yield","author":"Sudduth","year":"1996"},{"key":"B81","doi-asserted-by":"publisher","first-page":"100344","DOI":"10.1016\/j.xplc.2022.100344","article-title":"Proximal and remote sensing in plant phenomics: Twenty years of progress, challenges and perspectives","author":"Tao","year":"2022","journal-title":"Plant Commun."},{"key":"B82","doi-asserted-by":"publisher","first-page":"6299","DOI":"10.3390\/s22166299","article-title":"A cloud enabled crop recommendation platform for machine learning-driven precision farming","volume":"22","author":"Thilakarathne","year":"2022"},{"key":"B83","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.3390\/rs10111682","article-title":"High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model","volume":"10","author":"Thorp","year":"2018","journal-title":"Remote Sens."},{"key":"B84","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1007\/s10661-023-11536-8","article-title":"Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data","volume":"195","author":"Tunca","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"B85","doi-asserted-by":"publisher","DOI":"10.34133\/2020\/5801869","article-title":"Latent space phenotyping: automatic image-based phenotyping for treatment studies","volume":"2020","author":"Ubbens","year":"2020","journal-title":"Plant Phenomics"},{"key":"B86","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/B978-1-63067-038-2.00017-4","article-title":"The role of peanuts in global food security","author":"Valentine","year":"2016","journal-title":"Peanuts: Gene. Process. Util."},{"key":"B87","doi-asserted-by":"publisher","first-page":"2901","DOI":"10.1128\/aem.58.9.2901-2909.1992","article-title":"Dynamic mathematical model to predict microbial growth and inactivation during food processing","volume":"58","author":"Van Impe","year":"1992","journal-title":"Appl. Environ. Microbiol."},{"key":"B88","volume-title":"Python reference manual","author":"Van Rossum","year":"1995"},{"key":"B89","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.3390\/rs13091763","article-title":"Understanding growth dynamics and yield prediction of sorghum using high temporal resolution UAV imagery time series and machine learning","volume":"13","author":"Varela","year":"2021","journal-title":"Remote Sens."},{"key":"B90","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/978-3-319-63935-2_2","article-title":"Economic and academic importance of peanut","author":"Variath","year":"2017","journal-title":"Peanut Genome"},{"key":"B91","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1007\/s11119-020-09711-9","article-title":"Remote sensing and machine learning for crop water stress determination in various crops: a critical review","volume":"21","author":"Virnodkar","year":"2020","journal-title":"Precis. Agric."},{"key":"B92","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"B93","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.3390\/rs15123017","article-title":"Evaluating the effect of training data size and composition on the accuracy of smallholder irrigated agriculture mapping in Mozambique using remote sensing and machine learning algorithms","volume":"15","author":"Weitkamp","year":"2023","journal-title":"Remote Sens."},{"key":"B94","doi-asserted-by":"crossref","first-page":"134","DOI":"10.3146\/i0095-3679-8-2-15","article-title":"A non-destructive method for determining peanut pod maturity","volume":"8","author":"Williams","year":"1981","journal-title":"Peanut Sci."},{"key":"B95","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.molp.2020.01.008","article-title":"Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives","volume":"13","author":"Yang","year":"2020","journal-title":"Mol. Plant"},{"key":"B96","doi-asserted-by":"crossref","first-page":"777","DOI":"10.3390\/genes14040777","article-title":"Machine learning-assisted approaches in modernized plant breeding programs","volume":"14","author":"Yoosefzadeh Najafabadi","year":"2023","journal-title":"Genes"},{"key":"B97","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3390\/agriculture10010019","article-title":"BLOB-based AOMs: A method for the extraction of crop data from aerial images of cotton","volume":"10","author":"Young","year":"2020","journal-title":"Agriculture"},{"key":"B98","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.3390\/rs13152937","article-title":"Wheat yield prediction based on unmanned aerial vehicles-collected red\u2013green\u2013blue imagery","volume":"13","author":"Zeng","year":"2021","journal-title":"Remote Sens."},{"key":"B99","doi-asserted-by":"publisher","first-page":"758","DOI":"10.21475\/ajcs.2016.10.05.p7167","article-title":"Agronomic characteristics associated with the normalized difference vegetation index (NDVI) in the peanut crop","volume":"10","author":"Zerbato","year":"2016","journal-title":"Aust. J. Crop Sci."},{"key":"B100","doi-asserted-by":"publisher","first-page":"4511","DOI":"10.3390\/rs14184511","article-title":"Prediction of strawberry dry biomass from UAV multispectral imagery using multiple machine learning methods","volume":"14","author":"Zheng","year":"2022","journal-title":"Remote Sens."},{"key":"B101","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1128\/aem.56.6.1875-1881.1990","article-title":"Modeling of the bacterial growth curve","volume":"56","author":"Zwietering","year":"1990","journal-title":"Appl. Environ. Microbiol."}],"container-title":["Frontiers in Plant Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2024.1339864\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T04:29:52Z","timestamp":1708403392000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpls.2024.1339864\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":101,"alternative-id":["10.3389\/fpls.2024.1339864"],"URL":"https:\/\/doi.org\/10.3389\/fpls.2024.1339864","relation":{},"ISSN":["1664-462X"],"issn-type":[{"value":"1664-462X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,20]]},"article-number":"1339864"}}