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Prediction of Rice Yield Based on Multi-Source Data and Hybrid LSSVM Algorithms in China

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Abstract

Accurate prediction of rice yield is essential for national food security and the development of the national economy. Currently, owing to the influence of data sources and model parameters, it is difficult to obtain simple and highly accurate models for rice yield prediction. In this study, nine typical rice ecological observation stations in China were selected to build a rice yield prediction model integrating multi-source data based on the least squares support vector machine (LSSVM) model. To improve the accuracy of the rice yield prediction model, the genetic optimization algorithm (GA), particle swarm optimization algorithm (PSO), and grey wolf optimization algorithm (GWO) were selected to optimize the parameters of the least squares support vector machine model. The correlation significances of yield with different influencing factors followed the order: total solar radiation (Ra) > number of spikes (NS) > plant height (H) > average pressure (P) > maximum temperature (Tmax) > relative humidity (RH) > precipitation (Pre) > average surface temperature (Ts) > minimum temperature (Tmin) > sunshine hours (n) > accumulated temperature (Ta), and it was highly significant with meteorological data (P = 63.1%) and significant with phenotypic data (P = 36.9%). With an increasing number of influencing input factors, the model accuracy tended to increase and then decrease when prediction model was constructed. The results showed that in the input models with different variables, the prediction accuracy was the highest when the input was Ra, NS, H, P, Tmax, RH, Pre, Ts, Tmin, and n (R2 = 0.712–0.841, RMSE = 1.139–1.458 ton/ha, MAE = 0.814–1.085 ton/ha, and NSE = 0.702–0.831). With the reintroduction of input variables, the accuracy of the rice prediction model could not be significantly improved. Compared with stand-alone LSSVM models, hybrid optimization algorithms can significantly improve the accuracy of the LSSVM model prediction results. The results of the GA, GWO, and PSO algorithms optimized for LSSVM showed that GWO-LSSVM had the highest accuracy with R2 = 0.841, RMSE = 1.139 ton/ha, MAE = 0.814 ton/ha, and NSE = 0.831. The best accuracies of PSO and GA were R2 = 0.782, RMSE = 1.233 ton/ha, MAE = 0.882 ton/ha, NSE = 0.781, R2 = 0.818, RMSE = 1.169 ton/ha, MAE = 0.863 ton/ha, and NSE = 0.798. This study suggests that the optimization algorithm is important for optimizing the hyperparameter parameters of the LSSVM model and that the GWO˗LSSVM yield prediction model is recommended for predicting rice yields in China.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

Ra:

Total solar radiation

NS:

Number of spikes

H:

Plant height

P:

Average pressure

Tmax:

Maximum temperature

RH:

Relative humidity

Pre:

Precipitation

Ts:

Average surface temperature

Tmin:

Minimum temperature

n:

Sunshine hours

Ta:

Accumulated temperature

LSSVM:

The least squares support vector machine

GA:

The genetic optimization algorithm

PSO:

Article swarm optimization algorithm

GWO:

Grey wolf optimization algorithm

GA-LSSVM:

GA optimization model based on LSSVM

PSO-LSSVM:

PSO optimization model based on LSSVM

GWO-LSSVM:

GWO optimization model based on LSSVM

GBDT:

Gradient Boosting Decision Tree

F1:

Single factor of rice

F2:

Double factors of rice

F3:

Three factors of rice

F4:

Four factors of rice

F5:

Five factors of rice

F6:

Six factors of rice

F7:

Seven factors of rice

F8:

Eight factors of rice

F9:

Nine factors of rice

F10:

Ten factors of rice

F11:

Eleven factors of rice

R2 :

Coefficient of determination

RMSE:

Root mean square error

MAE:

Mean absolute error

NSE:

Nash–Sutcliffe efficiency

GPI:

Global performance indicator

MLR:

Multiple linear regression

RF:

Random forest

SVR:

Support vector regression

SVM:

Support vector machine

LSTM:

Long and short-term memory

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Acknowledgements

We would like to thank the National Climatic Centre of the China Meteorological Administration for providing the climate database used in this study. This work was also supported by National Natural Science Foundation of China (Grant No. 51922072), Key R&D and Promotion Projects in Henan Province (Science and Technology Development) (Grant No. 222102110452 and 232102110264), PhD Research Startup Foundation of Henan University of Science and Technology (No. 13480025 & 13480033), Key Scientific Research Projects of Colleges and Universities in Henan Province (No.22B416002).

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Authors and Affiliations

  1. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan Province, 471000, China

    Long Zhao, Shunhao Qing, Fei Wang, Hao Ma & Yi Shi

  2. Luoyang Company, Henan Provincial Tobacco Company, Luoyang, Henan Province, 471000, China

    Hui Wang

  3. State Key Laboratory of Hydraulics and Mountain River Engineering and College of Water Resource and Hydropower, Sichuan University, Chengdu, Sichuan Province, 610065, China

    Ningbo Cui

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  1. Long Zhao
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Contributions

LZ conceptualization, methodology, supervision, funding acquisition. SQ writing—original draft, formal analysis, software. FW investigation, data curation, software. HW visualization, software. HM visualization, funding acquisition. YS investigation. NC software, writing—review and editing.

Corresponding author

Correspondence to Ningbo Cui.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix

Appendix

See below Appendix Fig. 7 and Table 6 here.

Fig. 7
figure 7figure 7figure 7

Box line plots of predicted and true values for the four models with different inputs

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Table 6 Coefficient of the fitted regression line between the predicted and true values of the model for different combinations of inputs
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Zhao, L., Qing, S., Wang, F. et al. Prediction of Rice Yield Based on Multi-Source Data and Hybrid LSSVM Algorithms in China. Int. J. Plant Prod. 17, 693–713 (2023). https://doi.org/10.1007/s42106-023-00266-z

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