Machine Learning Models for Yield Estimation of Hybrid and Conventional Japonica Rice Cultivars Using UAV Imagery

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R 2 for CC based on the RF algorithm exceeded 0.9, while R 2 values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R 2 = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production.