Assessing the Potential of Drone Remotely Sensed Data in Detecting the Soil Moisture Content and Taro Leaf Chlorophyll Content Across Different Phenological Stages
Year: 2025
Type: article
Abstract: Soil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R2 values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions.
Source: Water
DOI: 10.3390/w17192796
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Life on land
Open Access status: gold
Estimated APC paid: $3,137
Funders: Wellcome Trust, Water Research Commission
OpenAlex ID: W4414448449