Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems

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.

Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform

“…The prime activity that locals of Swayimani partake in is semi-subsistence farming on their housing plots, which is a crucial form of livelihood sustenance and food security. The predominant crop types in Swayimani are white and yellow maize, sugarcane, tomatoes, amadumbe (taro), and sweet potato [28]. The smallholder farmers manually sow crop seeds and fertilise croplands with livestock manure.…”

“…In recent years, unmanned aerial vehicles (UAVs) have become a popular field phenotyping platform for precision agricultural applications. UAV-based phenotyping is the measurement and analysis of plant characteristics in a far more advanced and detailed manner [23,24,[28][29][30][31][32][33][34][35]. UAVs mounted with very-high-resolution (VHR) cameras offer advanced crop image throughput analytics and are effective in overcoming the limitations of satellite imagery [36,37].…”

Deep Learning Driven Crop Classification and Chlorophyll Content Estimation for the Nexus Food higher Productions using Multi-spectral Remote Sensing Images

“…Brewer et al [13] estimated the efficacy of multi-spectral UAV images with the random forest machine learning technique for estimating the chlorophyll content of maize via different development stages. The result shows that the red-edge and near-infrared wavelength bands and vegetation indices derived from the wavelength are needed to estimate chlorophyll content under the maize phenotyping.…”

Analysing Maize Plant Height Using Unmanned Aerial Vehicle (UAV) RGB based on Digital Surface Models (DSM)

“…Several studies presented the potential of using the UAV in maize studies such maize plant counting or maize growth surveillance [7,8]. Various findings also showed that the UAV could be used widely, due to the availability of flying in a certain altitude, frequently flights with different parameters and high-resolution results [9,10]. Different methods and tools were elaborated in maize plant studies, such as utilizing Faster-RCNN to detect maize plant density [11], the level of nitrogen and plant height by using machine learning approaches for example Random Forest (RF), or Support Vector Machine (SVM) [12].…”