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

Nduku, Lwandile; Munghemezulu, Cilence; Mashaba-Munghemezulu, Zinhle; Masiza, Wonga; Ratshiedana, Phathutshedzo Eugene; Kalumba, Ahmed Mukalazi; Chirima, Johannes George

doi:10.3390/land13030299

Cited by 2 publications

(2 citation statements)

References 112 publications

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“…Future research may consider including seasonal crop growth datasets, meteorological data, identification of crop disease, and other crop structural parameters including leaf chlorophyll content (LCC), the leaf area index (LAI), and crop density for holistic understanding of crop growth monitoring. Such datasets will be useful to famers when identfying appropriate windows for early production assessment [64].…”

Section: Discussionmentioning

confidence: 99%

“…The Clarens wheat farm area receives approximately 688 mm yearly mean rainfall and has 7.8 • C and 20.7 • C minimum and maximum mean temperatures for both winter and summer periods [61]. Rainfall occurs in the summer season with hot days above 20.7 • C as the average temperature [61][62][63][64]. Sandy loam, Avalon, and Pinedene are dominant soil types in the Thabo Mofutsanyane district.…”

Section: Location Of the Study Sitementioning

confidence: 99%

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Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Nduku,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2024

AgriEngineering

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Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.

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Section: Discussionmentioning

confidence: 99%

Section: Location Of the Study Sitementioning

confidence: 99%

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Nduku,

Munghemezulu,

Mashaba-Munghemezulu

et al. 2024

AgriEngineering

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Machine Learning Based Peach Leaf Temperature Prediction Model for Measuring Water Stress

Kim,

Kim

et al. 2024

Water

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When utilizing the Crop Water Stress Index (CWSI), the most critical factor is accurately measuring canopy temperature, which is typically done using infrared sensors and imaging cameras. In this study, however, we aimed to develop a machine learning model capable of predicting leaf temperature based on environmental data, without relying on sensors, for calculating CWSI. The data underwent preprocessing to remove outliers and missing values. The number of training data points for each factor was 307,924. After data preprocessing, a Pearson correlation analysis (bivariate correlation coefficient) was conducted to select the training data for model operation. The relationship between leaf temperature and air temperature showed a strong positive correlation of 0.928 (p < 0.01). Solar radiation and relative humidity were also found to have high correlations. However, wind speed and soil moisture tension showed very low correlations with leaf temperature and were excluded from the model operation. The Decision Tree, Random Forest, and Gradient Boosting models were selected, and each model was evaluated using RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Squared Error), and R2 (coefficient of determination). The evaluation results showed that the Gradient Boosting model had a high R2 (0.97) and low RMSE (0.88) and MAE (0.54), making it the most suitable model for predicting leaf temperature. Through the leaf temperature prediction model developed in this study, labor and costs associated with sensors can be reduced, and by applying it to real agricultural settings, it can improve crop quality and enhance the sustainability of agriculture.

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

Cited by 2 publications

References 112 publications

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning

Machine Learning Based Peach Leaf Temperature Prediction Model for Measuring Water Stress

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