Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

Abstract

The evaluation of the forage quality nutritive value and biomass usually takes multiple harvests and is considered time consuming, labor intensive, and expensive. The use of sensors to evaluate different forage traits has been proposed as a method to alleviate this problem. However, most analytical techniques involve the use of traditional linear methods for prediction, but prediction models can still be improved with the use of nonlinear methods. Thus, the objectives of this study are twofold: (i) to evaluate the performance of different prediction methodologies in important forage agronomic traits; and (ii) to evaluate the impact of sensor variables on predictive model performance in the different methods. The nondestructive multisensor system that accommodates spectral, ultrasonic, and laser data was tested on a bermudagrass [Cynodon dactylon (L.) Pers] experiment. Partial least square regression, ridge regression, support vector machine with radial kernel (SVM), and random forest were tested for the prediction performance. The random forest had the best performance, with 0.89 correlation for dry matter yield trait prediction, whereas SVM performed best in the remaining 15 traits, with correlation ranging from 0.72 to 0.95. Besides the prediction performance of statistical models, this study also provided some insight about the importance of variables by removing variables and reevaluating the model performance in dry matter yield. Overall, most of the traits in this study could be reliably predicted by the SVM. The multisensor system can facilitate the measurement of agriculturally important traits in a time‐, labor‐ and cost‐efficient way.

Evaluating Empirical Regression, Machine Learning, and Radiative Transfer Modelling for Estimating Vegetation Chlorophyll Content Using Bi-Seasonal Hyperspectral Images

“…The RFR performed the best, acquiring an R 2 of 0.86 and RMSE of 12.1 µ g/cm 2 for the June image and an R 2 of 0.81 and RMSE of 15.4 µ g/cm 2 for the August image (Figure 8). This is similar to the result in previous studies that RFR had a higher accuracy than that of other models for estimating vegetation properties [17,19]. The good performance of RFR is probably owning to its advantages in using randomly selected subset training data at each node and in selecting the best-performing predictor variable for splitting the node (thus not being sensitive to noise), as well as its ability to handle the collinearity problem [44].…”

“…The predicted residual error sum of squares (PRESS) statistics was applied to identify the optimal number of latent variables in PLSR that has the lowest model error. Since some predictor variables may not contribute to the regression model or may bring in noise, a backward feature elimination method was applied to eliminate the less promising variables and identify the model that achieves the highest accuracy [19,48]. The variable importance on projection (VIP) values were calculated in PLSR to evaluate effects of variables on the regression model [90].…”

“…The predictor variables used in PLSR, including VIs, reflectance, PCs, and textural variables, were also used in RFR. Importance values of all predictor variables were calculated in RFR [44], and then the backward feature elimination method was applied to RFR to progressively remove the least important variables [19,33,34,41]. The model with the lowest RMSE was regarded as the optimal model.…”

“…Reddy et al [18] applied PLSR and RFR for modelling forest structural attributes (e.g., volume, tree height) using airborne multispectral imagery. Xing et al [19] tested performance of PLSR, SVM, RFR, and ridge regression for estimating nutrition components (e.g., protein and sugar) in forage crops using a few field-based multispectral sensors and active sensors (e.g., laser). Yue et al [13] examined ANN, MLR, DTR, PLSR, RFR, SVM, boosted binary regression tree (BBRT), and PCR for estimating wheat biomass using field-measured hyperspectral reflectance.…”

Predicting Phosphorus and Potato Yield Using Active and Passive Sensors

“…The other had a regular camera/sensor used to collect the visible-color images of the red, green, and blue bands (Figure 1c). The gimbal controlled the UAV movement (pitch and roll) throughout the flight [51]. In 2019, A DJI Inspire 2 UAV (Shenzhen, Guangdong, China) with a portable Altum multispectral sensor (MicaSense, Seattle, WA, USA) was used to collect the multispectral data (Figure 1d).…”