Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.

Maize water stress from rainfall variability is a key challenge in producing rain-fed maize farming, especially in water-scarce regions, such as southern Africa. Hence, quantifying maize foliar water content variations throughout the phenological stages is valuable in detecting smallholder maize moisture stress and supporting agricultural decision-making. The emergence of unmanned aerial vehicles (UAVs) equipped with multispectral sensors offers a unique opportunity for robust and rapid monitoring of maize foliar water content and stress. The combination of near-real-time spatially explicit information acquired using UAV imagery with physiological indicators, such as equivalent water thickness (EWT) and fuel moisture content (FMC), provides viable options for detecting and quantifying maize foliar water content and moisture stress in smallholder farming systems. Therefore, we evaluated the utility of UAV-based multispectral datasets and random forest regression in quantifying maize EWT and FMC throughout the maize phenological growth cycle. Results showed that EWT and FMC could be determined using the near-infrared and red-edge wavelengths to a relative root mean square error of 2.27% and 1%, respectively. Specifically, the spectra acquired during the early reproductive growth stages between silking and milk stages demonstrated a high sensitivity to the variation in maize moisture content. These findings serve as a fundamental step toward creating an early maize moisture stress detection and warning system and contribute to climate change adaptation and resilience of smallholder maize farming.

Purpose: Taro (Colocasia esculenta (L)) , a neglected and underutilized crop species (NUS), holds great potential as a future smart crop that can thrive under climate variability and change, hence sustaining food security. While taro exhibits tolerance to drought conditions, variations in physiological attributes such as leaf temperature that rises under water stress and the associated stomatal closure that is initiated to conserve water, compromise crop productivity and overall yield. Therefore, monitoring taro crop physiological indicators of water status allows for the implementation of timely interventions and targeted adaption strategies to mitigate the effects of water deficit on taro crop productivity. Methods: Unmanned Aerial Vehicles (UAV), integrated with high-resolution thermal sensors, provide valuable platform for generating near-real-time spatially explicit information suitable for assessing taro crop water status physiological indicators at farm scale. Hence, this study sought to evaluate the utility of UAV multi-modal thermal remote sensing and deep neural network techniques to estimate the equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and the chlorophyll content of smallholder taro crops. Results: Findings showed that the multi-modal variable method achieves higher estimation accuracies in comparison to a single-modal technique, achieving R ² values greater than 0.91 and rRSME values less than 14.15% of equivalent water thickness, fuel moisture content, stomatal conductance, canopy temperature, and chlorophyll content. Additionally, the results illustrated that the thermal wavebands and derived thermal indices are the most influential variables in estimating stomatal conductance and leaf temperature, yielding R ² of 0.96 and 0.95, respectively. Conclusion: These research findings underscore the applicability of UAV-acquired thermal remote sensing in providing rapid and robust spatially explicit information on smallholder taro crop water status for ensuring crop productivity and developing early warning systems of water stress. These findings serve as a stepping stone towards advancing agricultural monitoring frameworks and integrating NUS, such as taro, into traditional farming.