Abstract
Smallholder 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.
Highlights
Smallholder agricultural systems contribute significantly to developing nations’ agricultural production, livelihood sustenance, and socio-economic growth [1]
This study investigates the potential of multispectral unmanned aerial vehicles (UAVs) imagery to assess maize-crop chlorophyll content using the random forest model simulation for an improved understanding of crop health and productivity in smallholder farming systems
The synergistic use of UAV remotely sensed technology and crop-health proxies such as chlorophyll content have facilitated a deeper understanding of crop dynamics
Summary
Smallholder agricultural systems contribute significantly to developing nations’ agricultural production, livelihood sustenance, and socio-economic growth [1]. Maize (Zea mays L.) is one of Remote Sens. Smallholder farmers typically cultivate maize under rainfed conditions, maximizing production and producing healthy crop yields. Despite the goals of smallholder farmers to optimize yields, small-scale farming systems often face a variety of challenges [3,6]. Their dependence on rainfall poses a significant threat to crop yields, as reduced seasonal rainfall and severe weather phenomena impact crop health, biochemical processes, and physical development [7,8]
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