Comparing Machine Learning Algorithms for Estimating the Maize Crop Water Stress Index (CWSI) Using UAV-Acquired Remotely Sensed Data in Smallholder Croplands

Abstract

Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R2 = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R2 = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions.

Remote Sensing-Based Monitoring of Cotton Growth and Its Response to Meteorological Factors

“…Remote sensing technology enables rapid and comprehensive monitoring of crop information [29][30][31][32][33][34]. The application of remote sensing technology in agriculture has advanced agricultural development [35][36][37].…”