Rice nitrogen nutrition estimation with RGB images and machine learning methods

Crop red-green-blue (RGB) images are powerful tools in nitrogen (N) nutrition estimation. Various regression models using crop N nutrition parameters and image indices have been suggested, but their accuracy and generalization performance for N estimation have not been thoroughly evaluated. In this study, a commercial digital camera was used to capture rice canopy RGB images in a 2-year field experiment, and three regression methods (simple nonlinear regression, SNR; backpropagation neural network, BPNN; and random forest regression, RF) were used for rice shoot dry matter (DM), N accumulation (NA), and leaf area index (LAI) estimation. A repeated random subsampling validation method was performed 1000 times on all three regression methods for the evaluation of model performance and stability. The RF regression models had the highest accuracy for the validation dataset, with average testing prediction accuracy (ATPA) of 80.17%, 79.44%, and 81.82% for DM, LAI, and NA estimation, respectively, followed by BPNN and SNR models. According to the distribution of ATPA in 1000-time calculations, the highest standard deviation (SD) and interval range (5%- 95%) of ATPA was observed in BPNN models, which indicated that the BPNN model was most susceptible to dataset splitting. The lower SD and interval range of ATPA were followed by RF and SNR models, which indicated that the RF and SNR models were less affected by dataset splitting and were able to produce robust regression models consistently. In conclusion, the ensemble algorithm of the RF model effectively prevents overfitting when dealing with different dataset segmentations; thus, the RF model has strong generalization performance. A combination of digital imagery and appropriate machine learning methods facilitates convenient and reliable estimation of crop N nutrition.

Automated summary

The random forest regression models showed the highest accuracy for estimating DM, LAI, and NA in the validation dataset, followed by BPNN and SNR models. The BPNN models were found to be most susceptible to dataset splitting, with the highest standard deviation and interval range of ATPA among the three regression methods, while RF and SNR models were less affected by dataset splitting, producing consistently robust regression models. The ensemble algorithm of the RF model effectively prevented overfitting and demonstrated strong generalization performance, highlighting the potential of combining digital imagery with machine learning methods for crop N nutrition estimation.