Estimation of nitrogen content in wheat using indices derived from RGB and thermal infrared imaging

Abstract

The important period of wheat grain accumulation is from the flowering stage to the filling stage, and the nitrogen content of wheat in this period is of great significance to the yield accumulation. With the rapid development of sensor technology, different sensors have been increasingly used for crop nitrogen status estimation due to their flexibility. This study aimed to investigate the use of a combination of image information from two proximal sensors (RGB and thermal sensors) to assess the nitrogen status of wheat at the reproductive growth stage. Previous studies have focused on estimating leaf N status at the nutritional growth stage of wheat, and the precision of N estimation is not high at the later stages. Considering that the canopy was composed of leaves and spikes in the reproductive stage, we integrated leaf N content and spike N content as plant N content for assessment. A two-year field trial was conducted, and this study used a Sony camera to acquire RGB images from flowering to maturity and obtained thermal images using the handle thermal infrared camera during the same period. Then, these images were further processed to extract the color features (17), the texture features (5) and temperature values (2). Based on these 24 indices, this study used three machine learning algorithms (i.e., Back-Propagation neural network (BP), Random Forest (RF) and Support Vector Regression (SVR)), resulted in nine estimation models based on a single dataset (i.e., c-based BP, te-based BP, t-based BP, c-based RF, te-based RF, t-based RF, c-based SVR, te-based SVR, t-based SVR) and 12 models based on data fusions (i.e., c+te-based BP, c+t-based BP, te+t-based BP, c+te+t-based BP, c+te-based RF, c+t-based RF, te+t-based RF, c+te+t-based RF, c+te-based SVR, c+t-based SVR, te+t-based SVR, c+te+t-based SVR). The performance of the 21 models was evaluated and compared with each other according to the coefficient of determination (R 2 ), root mean square error (RMSE) and residual prediction deviation (RPD) in nitrogen content estimation. The results show that the best model was the c+te+t-based RF, which was a model based on the combination of color features, texture features and temperature values. It achieved high accuracy in estimating plant N content (R 2 = 0.89, RMSE = 3.23 mg g −1 , RPD = 1.90). In conclusion, the combination of information from RGB and thermal images has good potential for application in monitoring crop N content at late reproductive stages, and plant temperature values can be used as effective indicators for assessing crop growth and nitrogen nutrient status. • Wheat temperature and temperature difference have the potential to monitor wheat growth. • Multi-source data (color, texture, and temperature information) based on the source-sink level have a good potential for monitoring nitrogen nutrient status in the later stages of wheat. • The fusion of information from RGB and thermal images has promising applications for monitoring the nitrogen nutrient status of crops. • The machine-learning models incorporating multiple features significantly improved the accuracy of the models for estimating nitrogen content in wheat plants.