Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems

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dc.creatorBrewer, K.
dc.creatorClulow, A.D.
dc.creatorSibanda, M.
dc.creatorGokool, S.
dc.creatorNaiken, V.
dc.creatorMabhaudhi, Tafadzwanashe
dc.date2022-01-21
dc.date2022-01-31T23:09:28Z
dc.date2022-01-31T23:09:28Z
dc.date.accessioned2026-06-27T18:30:28Z
dc.descriptionSmallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m-2 , 39 µmol/m-2 , and 61.6 µmol/m-2 , respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m-2 and 69.6 µmol/m-2 , respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.
dc.identifierhttps://hdl.handle.net/10568/117858
dc.identifier.urihttp://hdl.handle.net/123456789/157930
dc.languageen
dc.publisherMDPI
dc.rightsOpen Access
dc.sourceBrewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Naiken, V.; Mabhaudhi, Tafadzwanashe. 2022. Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems. Remote Sensing, 14(3):518. [doi: https://doi.org/10.3390/rs14030518]
dc.subjectmaize
dc.subjectchlorophylls
dc.subjectplant health
dc.subjectforecasting
dc.subjectsmallholders
dc.subjectfarming systems
dc.subjectprecision agriculture
dc.subjectmachine learning
dc.subjectunmanned aerial vehicles
dc.titlePredicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems
dc.typeJournal Article

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Instituto Internacional para el Manejo del Agua (IWMI)