Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

Lwandile Nduku (), Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Wonga Masiza, Phathutshedzo Eugene Ratshiedana, Ahmed Mukalazi Kalumba and Johannes George Chirima
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Lwandile Nduku: Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Pretoria 0028, South Africa
Cilence Munghemezulu: Geoinformation Science Division, Agricultural Research Council, Institute for Soil, Natural Resources and Engineering, Pretoria 0001, South Africa
Zinhle Mashaba-Munghemezulu: Geoinformation Science Division, Agricultural Research Council, Institute for Soil, Natural Resources and Engineering, Pretoria 0001, South Africa
Wonga Masiza: Geoinformation Science Division, Agricultural Research Council, Institute for Soil, Natural Resources and Engineering, Pretoria 0001, South Africa
Phathutshedzo Eugene Ratshiedana: Geoinformation Science Division, Agricultural Research Council, Institute for Soil, Natural Resources and Engineering, Pretoria 0001, South Africa
Ahmed Mukalazi Kalumba: GACCES Lab, Department of Geography & Environmental Science, University of Fort Hare, Alice 5700, South Africa
Johannes George Chirima: Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Pretoria 0028, South Africa

Land, 2024, vol. 13, issue 3, 1-26

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 (R 2 = 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.

Keywords: winter wheat; crop growth; vegetation indices; soil properties; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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