Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

Wang, Sijia; Chen, Yunhao; Wang, Mingguo; Li, Jing

doi:10.3390/rs11222605

Cited by 29 publications

(16 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, four RCRW a-b parameters (RCRW 532-550 , RCRW 665-674 , RCRW 691-698 , and RCRW 738-747 ) were determined by the same method. Based on the four selected RCRW a-b , combined with the gradient boosting regression tree (GBRT) algorithm [38,39], we obtained the spatiotemporal distribution of SPAD values in Plot 1 and Plot 2.…”

Section: Estimating Rice Spad Based On Rcrw A-bmentioning

confidence: 99%

“…The core idea of GBRT is that each calculation is completed by a basic model, and the subsequent calculation is done to reduce the residual of the previous model and create a new basic model with the reduced residual in the gradient direction. By adjusting the weight of the weak prediction model, the final strong prediction model can be obtained [38,39]. The contributions of decision trees (learning_rate), the number of sub-decision trees (n_estimators), and the maximum depth of each decision tree (max_depth) are the important parameters in GBRT [28].…”

Section: Estimating Rice Spad Based On Rcrw A-bmentioning

confidence: 99%

See 1 more Smart Citation

Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Xing

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.

show abstract

Section: Estimating Rice Spad Based On Rcrw A-bmentioning

confidence: 99%

Section: Estimating Rice Spad Based On Rcrw A-bmentioning

confidence: 99%

Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Xing

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The gradient boosting regression tree (GBRT) is an iterative decision tree algorithm composed of multiple decision trees, which makes GBRT difficult for parallel computing. The core idea of the GBRT is that each calculation is done by a basic model, and the subsequent calculation is undertaken to reduce the residual of previous model and to create a new basic model in the direction of gradient with reduced residuals [38,39]. Therefore, a strong prediction model can be obtained by adjusting the weight of the weak prediction model.…”

Section: Gradient Boosting Regression Treementioning

confidence: 99%

Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

Xing

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.

show abstract

“…The random forest regression (RFR) [30,31] is an integrated classifier composed of multiple decision trees based on bagging. There is no correlation between the decision trees.…”

Section: Rfr Modelmentioning

confidence: 99%

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Wang

Chen

et al. 2022

Agriculture

View full text Add to dashboard Cite

Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

show abstract

Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

Cited by 29 publications

References 56 publications

Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Contact Info

Product

Resources

About