Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn

Abstract

<jats:p>The optimization of crop nitrogen fertilization to accurately predict and match the nitrogen (N) supply to the crop N demand is the subject of intense research due to the environmental and economic impact of N fertilization. Excess N could seep into the water supplies around the field and cause unnecessary spending by the farmer. The drawbacks of N deficiency on crops include poor plant growth, ultimately reducing the final yield potential. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery to predict canopy nitrogen weight (g/m2) of corn fields in south-west Ontario, Canada. Simple/multiple linear regression, Random Forests, and support vector regression (SVR) were established to predict the canopy nitrogen weight from individual multispectral bands and associated vegetation indices (VI). Random Forests using the current techniques/methodologies performed the best out of all the models tested on the validation set with an R2 of 0.85 and Root Mean Square Error (RMSE) of 4.52 g/m2. Adding more spectral variables into the model provided a marginal improvement in the accuracy, while extending the overall processing time. Random Forests provided marginally better results than SVR, but the concepts and analysis are much easier to interpret on Random Forests. Both machine learning models provided a much better accuracy than linear regression. The best model was then applied to the UAV images acquired at different dates for producing maps that show the spatial variation of canopy nitrogen weight within each field at that date.</jats:p>

Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

“…On the other hand, Niu et al [25] carried out a comparison between three kind of models proposed in this research, multiple linear regression, artificial neural network and support vector machine in the hydropower operation field and they concluded that the artificial neural networks and the support vector machine provide better performances than the MLR model [25]. A similar treatment can be seen in the research carried out by Lee et al [26] in which these researchers developed simple/multiple LR, RF and SVM models to predict canopy nitrogen weight in corn using multispectral images obtained by an unmanned aerial vehicle. Authors concluded that the RF models presented the best results for the validation set and verified that when more spectral variables were used the model improved the accuracy and make longer the overall processing time [26].…”

“…A similar treatment can be seen in the research carried out by Lee et al [26] in which these researchers developed simple/multiple LR, RF and SVM models to predict canopy nitrogen weight in corn using multispectral images obtained by an unmanned aerial vehicle. Authors concluded that the RF models presented the best results for the validation set and verified that when more spectral variables were used the model improved the accuracy and make longer the overall processing time [26].…”

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

“…Assessing the tabular machine learning models independently, Random forest outperformed XGBoost and tab-DNN in this dataset, indicating its effectiveness for crop yield prediction using a single type of data. The random forest method has been extensively applied for crop yield prediction [4,[49][50][51][52][53], with previous studies showing it to outperform other machine learning algorithms in selected datasets [50,53]. XGBoost had the worst performance among the machine learning models tested, placing the samples into yield groups.…”

Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method

“…Powerful in self-adaptive capability, machine-learning methods are popularly applied in land-use classification. Random Forests, support vector machines, and artificial neural networks have made a great contribution to land-cover/land-use classification [4][5][6][7][8][9][10][11]. The support vector machine (SVM) is applied to reduce the execution time of storing and processing hyperspectral images [11].…”

“…The support vector machine (SVM) is applied to reduce the execution time of storing and processing hyperspectral images [11]. Simple/multiple linear regression, random forest (RF), and support vector regression (SVR) were used to estimate canopy nitrogen weight of maize leaves, and the results showed that both machine learning models performed much better than linear regression [4]. Multisource remote sensing imagery was used to obtain a wetland species map using an RF classifier [9].…”