Research on rice leaf area index estimation based on fusion of texture and spectral information

Abstract

Leaf Area Index (LAI) is one of the indicators used to measure the growth status of rice fields. Rapid, accurate, and large-scale monitoring of LAI plays an important role in ensuring stable grain yield increase. In recent years, the spectral saturation problem and the parameter adjustment problem of machine learning algorithms have become the main limitations to improve the accuracy of LAI estimation. High-resolution Unmanned Aerial Vehicles (UAVs) images contain not only rich spectral information, but also texture information reflecting the crop canopy structure. Therefore, in order to fully understand the role of spectral information and texture information fusion in rice LAI estimation, this study used the hyperspectral sensor carried by the UAVs to obtain the spectral images of rice canopy of different varieties and different growth stages. Rice canopy reflectance and 8 basic texture features based on Gray-level Co-occurrence Matrix (GLCM) were extracted from hyperspectral images to calculate vegetation indexs (VIs) and combined texture features. Normalized difference texture index (NDTI), Non-linear texture index (NLTI), Enhanced vegetation texture index (EVTI), and Modified triangular texture index (MTTI) were calculated using two and three GLMC-based texture features to explore the effect of combinations of different basic texture features on LAI sensitivity. Two rice LAI estimation models were developed for single spectral indicators and combined with texture indicators, respectively. The results show that: (1) After preprocessing and feature band screening, the optimal spectral band, vegetation index, and trilateral parameters were obtained. When the combined spectral parameters (SP) of the three were used as the only input to the model, R2 showed an increasing trend throughout the growth period. The best results were achieved using the support vector regression (SVR) combined with the pelican optimization algorithm (POA) in the pre jointing stage: R2= 0.839, RMSE = 0.107, and MAPE = 7.02%. (2) When texture information based on hyperspectral images was incorporated into the model input, the results showed that the models based on spectral indicators combined with texture measurements were all superior to those using spectral indicators alone, with the best model having a coefficient of determination: R2 = 0.917, RMSE = 0.078, and MAPE = 4.19%, which has promising applications in crop growth index detection. This technology can quickly and effectively monitor the growth status of crops in the field, providing a theoretical basis for estimating crop yield in the later stage.