Rice Yield Estimation Based on Continuous Wavelet Transform With Multiple Growth Periods

Chen Gu; Shu Ji; Xiaobo Xi; Zhenghua Zhang; Qingqing Hong; Zhongyang Huo; Wenxi Li; Wei Mao; Haitao Zhao; Ruihong Zhang; Bin Li; Changwei Tan

doi:10.3389/fpls.2022.931789

Rice Yield Estimation Based on Continuous Wavelet Transform With Multiple Growth Periods

Front Plant Sci. 2022 Jul 1:13:931789. doi: 10.3389/fpls.2022.931789. eCollection 2022.

Authors

Chen Gu¹, Shu Ji¹, Xiaobo Xi¹, Zhenghua Zhang¹, Qingqing Hong¹, Zhongyang Huo¹, Wenxi Li², Wei Mao², Haitao Zhao¹, Ruihong Zhang¹, Bin Li¹, Changwei Tan¹

Affiliations

¹ Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education of China, Key Laboratory of Cultivated Land Quality Monitoring and Evaluation (Jiangsu) Ministry of Agriculture and Rural Affairs, Jiangsu Engineering Centre for Modern Agricultural Machinery and Agronomy Technology, Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou, China.
² Station of Land Protection of Yangzhou City, Yangzhou, China.

Abstract

Yield is an important indicator in evaluating rice planting, and it is the collective result of various factors over multiple growth stages. To achieve a large-scale accurate prediction of rice yield, based on yield estimation models using a single growth stage and conventional spectral transformation methods, this study introduced the continuous wavelet transform algorithm and constructed models under the premise of combined multiple growth stages. In this study, canopy reflectance spectra at four important stages of rice elongation, heading, flowering and milky were selected, and then, a rice yield estimation model was constructed by combining vegetation index, first derivative and wavelet transform based on random forest algorithm or multiple stepwise regression. This study found that the combination of multiple growth stages significantly improved the model accuracy. In addition, after two validations, the optimal model combination for rice yield estimation is first derivative-wavelet transform-vegetation index-random forest model based on four growth stages, with the coefficient of determination (R²) of 0.86, the root mean square error (RMSE) of 35.50 g·m^-2 and the mean absolute percentage error (MAPE) of 4.6% for the training set, R² of 0.85, RMSE of 33.40 g.m^-2 and MAPE 4.30% for the validation set 1, and R² of 0.80, RMSE of 37.40 g·m^-2 and MAPE of 4.60% for the validation set 2. The research results demonstrated that the established model could accurately predict rice yield, providing technical support and a foundation for large-scale statistical estimating of rice yield.

Keywords: hyperspectral; multi-growth stage; remote sensing; rice; wavelet transform; yield.