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The study evaluates how unmanned aerial vehicle (UAV) data combined with machine learning estimates yield and characterizes spatiotemporal variability along the crop phenological cycle in Bronkhorstspruit, South Africa. UAV spectral bands, vegetation indices, and GLCM texture features were computed across four dates, then features were selected via correlation with measured yield. Regression models including Random Forest, GradBoost, Categorical Boosting, and Extreme Gradient Boosting were tested, with GradBoost achieving the best accuracy (R2 0.05–0.67). Welch’s test showed significant yield differences from pre-flowering to maturity (p \u003C 0.01).","Article  \nAssessing Maize Yield Spatiotemporal Variability Using Unmanned Aerial Vehicles and Machine Learning  \nColette de Villiers 1,2, Zinhle Mashaba-Munghemezulu 1, *, Cilence Munghemezulu 1,3, George J. Chirima 1,2 and Solomon G. Tesfamichael 3  \nCitation: de Villiers, C.;  \nMashaba-Munghemezulu, Z.;  \nMunghemezulu, C.; Chirima, G.J.; Tesfamichael, S.G. Assessing Maize Yield Spatiotemporal Variability Using Unmanned Aerial Vehicles and Machine Learning. Geomatics 2024, 4, 213–236. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)geomatics4030012  \nAcademic Editor: Enrico Tomelleri  \nReceived: 7 May 2024  \nRevised: 14 June 2024  \nAccepted: 22 June 2024  \nPublished: 28 June 2024  \nCopyright: © 2024 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 Geoinformation Science Division, Agricultural Research Council, Natural Resources and Engineering,  \nPretoria 0001, South Africa; [colettedev007@gmail.com](colettedev007@gmail.com) (C.d.V.); [munghemezuluc@arc.agric.za](munghemezuluc@arc.agric.za) (C.M.);  \n[chirimaj@arc.agric.za](chirimaj@arc.agric.za) (G.J.C.)  \n2 Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0002, South Africa  \n3 Department of Geography, Environmental Management and Energy Studies, University of Johannesburg, Johannesburg 2006, South Africa; [sgtesfamichael@uj.ac.za](sgtesfamichael@uj.ac.za)  \n* [Correspondence: mashabaz@arc.agric.za](Correspondence: mashabaz@arc.agric.za)  \nAbstract: Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger) . This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity) . The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R2 ranging from 0 .05 to 0 .67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value \u003C 0.01) . These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.  \nKeywords: yield prediction; maize; growth stages; vegetation indices; unmanned aerial vehicles; machine learning algorithms; grey-level co-occurrence matrix (GLCM)  \n1. Introduction  \nMaize (Zea mays L.) is a crop of ","cbCaigU2b730ZhBi","https://ap.wps.com/l/cbCaigU2b730ZhBi","pdf",5171715,3,1,24,"English","en",105,"# Introduction\n## Maize importance and yield variability\n## Value of timely crop growth information\n## Remote sensing approaches for yield estimation\n# Materials and Methods\n## Study area and data collection\n## UAV spectral bands and derived features\n## Feature selection strategy\n## Machine learning regression models\n# Results\n## Model performance and accuracy\n## Spatiotemporal yield variability patterns\n## Statistical testing of growth-stage differences","[{\"question\":\"How was maize yield estimated in the study?\",\"answer\":\"UAV-derived spectral bands were used to compute vegetation indices and GLCM texture features, followed by correlation-based feature selection and regression modeling to estimate yield for each growth period.\"},{\"question\":\"Which machine learning model performed best?\",\"answer\":\"GradBoost regression delivered the best overall accuracy, with R2 values ranging from 0.05 to 0.67 and RMSE from 1.93 to 2.9 t/ha.\"},{\"question\":\"What did the study find about yield variability over growth stages?\",\"answer\":\"Higher predicted yield values were observed during the grain-filling and mature stages, and Welch’s test indicated statistically significant differences from pre-flowering to maturity (p \\u003c 0.01).\"}]","Assessing Maize Yield Spatiotemporal Variability Using Unmanned Aerial Vehicles and Machine Learning | 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was maize yield estimated in the study?","Question",{"text":76,"@type":77},"UAV-derived spectral bands were used to compute vegetation indices and GLCM texture features, followed by correlation-based feature selection and regression modeling to estimate yield for each growth period.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning model performed best?",{"text":81,"@type":77},"GradBoost regression delivered the best overall accuracy, with R2 values ranging from 0.05 to 0.67 and RMSE from 1.93 to 2.9 t/ha.",{"name":83,"@type":74,"acceptedAnswer":84},"What did the study find about yield variability over growth stages?",{"text":85,"@type":77},"Higher predicted yield values were observed during the grain-filling and mature stages, and Welch’s test indicated statistically significant differences from pre-flowering to maturity (p \u003C 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