Improving the performance of plant nitrogen assessment in drip-irrigated potatoes using optimized spectral indices-based machine learning

Timely and accurate monitoring of plant nitrogen concentration (PNC) is vital for optimizing field N management. Hyperspectral indices are commonly used as a predictor for monitoring the PNC of crops, but individual spectral indices are often susceptible to cultivars and growth stages. Machine learning (ML) is a promising method for mining more spectral variables to assess the PNC of crops. To monitor the PNC of potatoes, therefore, this study extended previous work to further use hyperspectral optimized spectral indices (OSI) as input variables of ML, while, comparing with the ML models that used full-spectrum (FS), existing spectral indices (ESI) and sensitive spectral bands (SSB) as input variables, as well as simple regression model based on OSI alone. The partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and artificial neural network (ANN) models were calibrated using a dataset encompassing three cultivars and critical fertigation growth stages under three to six N levels. The calibrated ML models were evaluated using the datasets from independent experiments and two farmers´ fields. The OSI as an input variable in ML models showed superiority for predicting the potato PNC compared to FS, SSB, and ESI. The OSI-based RF model with an R² of 0.79, RMSE of 0.27%, and RPD of 2.18 had higher accuracy for predicting potato PNC than other ML models. Comparing the simple optimized spectral indices regression model alone, the OSI-based RF model reduced RMSE by mitigating the effects of cultivars and growth stages on PNC prediction. The OSI-based RF model significantly contributes to optimum fertilization management based on actual potato N status during critical growth periods.