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Machine learning modelling of crop structure within the Maize Triangle of South Africa

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dc.contributor.author Naidoo, Laven
dc.contributor.author Main, Russell, S
dc.contributor.author Cho, Moses, A
dc.contributor.author Madonsela, Sabelo
dc.contributor.author Majozi, Nobuhle, P
dc.date.accessioned 2022-05-06T08:20:02Z
dc.date.available 2022-05-06T08:20:02Z
dc.date.issued 2022-01
dc.identifier.citation Naidoo, L., Main, R., Cho, M., Madonsela, S. & Majozi, N. 2022. Machine learning modelling of crop structure within the Maize Triangle of South Africa. <i>International Journal of Remote Sensing, 43(1).</i> http://hdl.handle.net/10204/12396 en_ZA
dc.identifier.issn 0143-1161
dc.identifier.issn 1366-5901
dc.identifier.uri https://doi.org/10.1080/01431161.2021.1998714
dc.identifier.uri http://hdl.handle.net/10204/12396
dc.description.abstract Maize has been identified as a strategic commodity for the reduction of poverty and enhancement of food security in the African continent. Climate variability and difficult economic conditions are pressuring farmers to produce higher (maize) yields with fewer inputs, per hectare. The remote sensing of crop specific structural parameters are essential in identifying the particular growth stages of the maize crop which require specific tasks from the farmer (e.g. weed control, top dressing, pesticide application for disease and borer control and critical moisture phase). This study sought to assess the performance of multiple linear regression (LR), Random Forest (RF) and Gaussian Process Regression (GPR) in the estimation of four maize crop structural parameters in a study area in the Vereeniging region of the Maize Triangle of South Africa. These parameters were leaf area index (LAI), stem height (HT), stem diameter (DIA) and stem density (SD). An additional aim was to investigate whether the combination of selected spectral vegetation indices (red-edge, chlorophyll, senescence and greenness) with Sentinel-2 reflectance bands as modelling predictors yielded improved results over the individual spectral bands alone. Combining reflectance bands and vegetation indices as modelling predictors yielded the highest validation accuracy, over other scenarios, for only one out of the four crop structural parameters (DAI). The reflectance bands only scenario yielded the highest validation accuracies for two crop structural parameters (HT and SD). The use of spectral vegetation indices alone as modelling predictors yielded the highest modelling accuracies for the LAI crop parameter than the other scenarios. These trends indicate that the combination of Sentinel-2 reflectance bands and derived vegetation indices do not always yield improved modelling results for the four crop structural parameters under investigation. As a result, reflectance bands (mostly) or indices alone could suffice for nearly all of the parameters. With respect to the modelling algorithms, LR yielded the highest accuracies for DIA and SD (Standard Error of Prediction or SEP values of 22.40%±4.65 and 34.15%±2.72 respectively). GPR yielded the highest accuracies for LAI and HT (SEP values of 28.69%±3.84 and 23.19%±2.27 respectively) while RF did not yield the highest validation accuracy for any of the crop structural parameters. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://www.tandfonline.com/doi/full/10.1080/01431161.2021.1998714 en_US
dc.source International Journal of Remote Sensing, 43(1) en_US
dc.subject Maize Triangle of South Africa en_US
dc.subject Food security en_US
dc.subject Climate change en_US
dc.title Machine learning modelling of crop structure within the Maize Triangle of South Africa en_US
dc.type Article en_US
dc.description.pages 27-51 en_US
dc.description.note © 2022 Informa UK Limited, trading as Taylor & Francis Group. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://www.tandfonline.com/doi/full/10.1080/01431161.2021.1998714 en_US
dc.description.cluster Advanced Food, Agriculture and Health en_US
dc.description.impactarea Precision Agriculture en_US
dc.identifier.apacitation Naidoo, L., Main, R., Cho, M., Madonsela, S., & Majozi, N. (2022). Machine learning modelling of crop structure within the Maize Triangle of South Africa. <i>International Journal of Remote Sensing, 43(1)</i>, http://hdl.handle.net/10204/12396 en_ZA
dc.identifier.chicagocitation Naidoo, Laven, Russell Main, Moses Cho, Sabelo Madonsela, and Nobuhle Majozi "Machine learning modelling of crop structure within the Maize Triangle of South Africa." <i>International Journal of Remote Sensing, 43(1)</i> (2022) http://hdl.handle.net/10204/12396 en_ZA
dc.identifier.vancouvercitation Naidoo L, Main R, Cho M, Madonsela S, Majozi N. Machine learning modelling of crop structure within the Maize Triangle of South Africa. International Journal of Remote Sensing, 43(1). 2022; http://hdl.handle.net/10204/12396. en_ZA
dc.identifier.ris TY - Article AU - Naidoo, Laven AU - Main, Russell, S AU - Cho, Moses, A AU - Madonsela, Sabelo AU - Majozi, Nobuhle, P AB - Maize has been identified as a strategic commodity for the reduction of poverty and enhancement of food security in the African continent. Climate variability and difficult economic conditions are pressuring farmers to produce higher (maize) yields with fewer inputs, per hectare. The remote sensing of crop specific structural parameters are essential in identifying the particular growth stages of the maize crop which require specific tasks from the farmer (e.g. weed control, top dressing, pesticide application for disease and borer control and critical moisture phase). This study sought to assess the performance of multiple linear regression (LR), Random Forest (RF) and Gaussian Process Regression (GPR) in the estimation of four maize crop structural parameters in a study area in the Vereeniging region of the Maize Triangle of South Africa. These parameters were leaf area index (LAI), stem height (HT), stem diameter (DIA) and stem density (SD). An additional aim was to investigate whether the combination of selected spectral vegetation indices (red-edge, chlorophyll, senescence and greenness) with Sentinel-2 reflectance bands as modelling predictors yielded improved results over the individual spectral bands alone. Combining reflectance bands and vegetation indices as modelling predictors yielded the highest validation accuracy, over other scenarios, for only one out of the four crop structural parameters (DAI). The reflectance bands only scenario yielded the highest validation accuracies for two crop structural parameters (HT and SD). The use of spectral vegetation indices alone as modelling predictors yielded the highest modelling accuracies for the LAI crop parameter than the other scenarios. These trends indicate that the combination of Sentinel-2 reflectance bands and derived vegetation indices do not always yield improved modelling results for the four crop structural parameters under investigation. As a result, reflectance bands (mostly) or indices alone could suffice for nearly all of the parameters. With respect to the modelling algorithms, LR yielded the highest accuracies for DIA and SD (Standard Error of Prediction or SEP values of 22.40%±4.65 and 34.15%±2.72 respectively). GPR yielded the highest accuracies for LAI and HT (SEP values of 28.69%±3.84 and 23.19%±2.27 respectively) while RF did not yield the highest validation accuracy for any of the crop structural parameters. DA - 2022-01 DB - ResearchSpace DP - CSIR J1 - International Journal of Remote Sensing, 43(1) KW - Maize Triangle of South Africa KW - Food security KW - Climate change LK - https://researchspace.csir.co.za PY - 2022 SM - 0143-1161 SM - 1366-5901 T1 - Machine learning modelling of crop structure within the Maize Triangle of South Africa TI - Machine learning modelling of crop structure within the Maize Triangle of South Africa UR - http://hdl.handle.net/10204/12396 ER - en_ZA
dc.identifier.worklist 25534 en_US


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