Abstract:
Monitoring crop growth conditions during the growing season provides information on
available soil nutrients and crop health status, which are important for agricultural management
practices. Crop growth frequently varies due to site-specific climate and farm management practices.
These variations might arise from sub-field-scale heterogeneities in soil composition, moisture levels,
sunlight, and diseases. Therefore, soil properties and crop biophysical data are useful to predict
field-scale crop development. This study investigates soil data and spectral indices derived from
multispectral Unmanned Aerial Vehicle (UAV) imagery to predict crop height at two winter wheat
farms. The datasets were investigated using Gaussian Process Regression (GPR), Ensemble Regression
(ER), Decision tree (DT), and Support Vector Machine (SVM) machine learning regression algorithms.
The findings showed that GPR (R2 = 0.69 to 0.74, RMSE = 15.95 to 17.91 cm) has superior accuracy
in all models when using vegetation indices (VIs) to predict crop growth for both wheat farms.
Furthermore, the variable importance generated using the GRP model showed that the RedEdge
Normalized Difference Vegetation Index (RENDVI) had the most influence in predicting wheat crop
height compared to the other predictor variables. The clay, calcium (Ca), magnesium (Mg), and
potassium (K) soil properties have a moderate positive correlation with crop height. The findings from
this study showed that the integration of vegetation indices and soil properties predicts crop height
accurately. However, using the vegetation indices independently was more accurate at predicting
crop height. The outcomes from this study are beneficial for improving agronomic management
within the season based on crop height trends. Hence, farmers can focus on using cost-effective VIs
for monitoring particular areas experiencing crop stress.
