Prediction of Leaf Water Content in Maize Seedlings Based on Hyperspectral Information

Abstract

To find a convenient and non-destructive way to detect the water stress status of maize plant, hyperspectral technology was used to detect maize leaf water content (LWC) in this paper. Experiments were conducted on maize at the seedling stage in Beijing, China. Firstly, hyperspectral images of 85 maize plant leaves were obtained. To remove redundant information, two methods were used for dimensionality reduction, which are principal component analysis (PCA) and kullback-leibler divergence (KLD) methods, resulting in 10 and 6 wavebands, respectively. These wavebands were then used to establish water stress detection model based on support vector regress (SVR) method. Particle swarm optimization (PSO) algorithm was applied to optimize the parameters for the support vector regression (SVR), including the penalty factor C and kernel function parameter g. Finally, the SVR model with the optimized parameters were trained with 65 pretreatment samples, and the generalization ability of the model was tested with the remaining 20 samples. Experimental results show the model built by bands selected by KLD method (R=0.7684) is better than PCA method(R=0.3513).Future research should aim at testing the model generation for different growing stage of maize.