Prediction of winter wheat nitrogen nutrition index using high-resolution satellite and machine learning

Abstract

Wheat (Triticum aestivum L.) is the most important cereal crop grown in Spain, and Spain is one of the top wheat-producing countries in EU. Precision fertilization, which customizes the fertilizer dosage based on the variability of the field, is important for the environment, food security, and farmers’ finances. To provide the fertilizer prescription, assessing crop nitrogen (N) status is required to make site-specific fertilizer applications. Among the nitrogen status index, the nitrogen nutrition index (NNI) is considered the most reliable to monitor the N status. Prior studies have used satellite, UAV, or spectral sensors with mostly adopting linear regression methods to predict NNI. However, no study has investigated the potential of using high-resolution satellite and environmental data with machine learning (ML) to predict winter wheat NNI. Therefore, this study integrated PlanetScope satellite images with weather data while adopting three ML algorithms, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to predict NNI in Spain from 2018 to 2019. The results showed that RF outperformed the SVM and ANN models with an accuracy of 77.08 % and a precision of 0.78. This study also demonstrated weather data improved model performance across all three algorithms with the highest accuracy of 79.12 % in the RF algorithm. Among all three algorithms, the elongation period outperformed the flowering period and across the entire period with an accuracy of 81.25 - 87.5 % and a precision of 0.5 - 0.78. In the end, the N status diagnostic map was generated to reflect the nitrogen requirement and provide a decision support tool for farmers before the fertilizer application. The proposed methodology in this paper can be extended to different crops and different regions for NNI prediction.