Improving rice yield and quality through high-throughput phenomics, linear regression, and machine learning neural network models

To explore the potential of using high-throughput plant phenomics in rice breeding programs, one hundred elite rice varieties from southern rice-growing areas in China were subjected to high-throughput phenomic analysis. A total of 88 parameters were measured and obtained using RGB imaging, fluorescence imaging, and hyperspectral imaging at four key rice growth stages: tillering, jointing, grain filling, and 20 days after grain filling. These 88 parameters, which include RGB color and morphological features, chlorophyll fluorescence characteristics, and rice surface reflectance spectra, were analyzed to characterize high yield and high grain quality in rice using subset selection regression and deep learning neural network models. A total of 39 significant linear regression models were obtained for predicting rice yield and grain quality, with R-squared values ranging from 0.86 to 0.15, and an average R-squared of 0.41. The data from the 100 rice varieties were split into training and test sets to evaluate the prediction accuracies of the models using mean absolute error between predicted and actual values. The results indicated that the deep learning neural network model can be used to refine the linear regression model, improving the prediction accuracy. These findings suggest that high-throughput plant phenomics can be effectively utilized in rice breeding programs to select for high-yielding, high-quality rice varieties.