Browsing by Author "Ndlovu, Helen S"

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    Enhancing the estimation of equivalent water thickness in neglected and underutilized taro crops using UAV acquired multispectral thermal image data and index-based image segmentation
    (Elsevier B.V., 2025) Sibanda, Mbulisi; Ndlovu, Helen S; Odindi, John
    Taro, recognized as a future smart neglected and underutilized crop as a result of its resilience to abiotic stresses, has emerged as valuable for diversifying crop farming systems and sustaining local livelihoods. Nonetheless, a significant research gap exists in spatially explicit information on the water status of taro, contributing to the paradox of its ability to adapt to diverse agro-ecological conditions. Precision agriculture, including the use of unmanned aerial vehicles (UAVs) outfitted with high-resolution multispectral and thermal imagery, has proven effective in farm-scale monitoring and provides near-real-time information on crop water status. Hence, this study sought to evaluate the applicability of multispectral and thermal infrared UAV imagery in understanding taro's water status. Leveraging deep learning techniques to evaluate the use of thermal remote sensing and three index-based segmentation techniques in predicting the canopy equivalent water thickness (EWT) of taro crops, this study sought to determine EWT as a proxy to its water status in smallholder farmlands. The study findings illustrate a significant difference in the prediction accuracies of taro EWT with and without the thermal band ( P < 0.05 ). Additionally, results (R2 = 0.92, RMSE = 8.04 g/m2, and rRMSE = 15.31 % including the thermal band and 0.91, 8.73 g/m2, and 16.64 % excluding the thermal band) reveal the value of the Excess Green minus Excess Red (ExGR) technique in accurately predicting EWTcanopy. This study serves as a foundation for developing an effective and efficient monitoring framework that provides a spatially explicit overview of neglected and underutilized crops such as taro.