Wheat leaf layer nitrogen content estimation method based on hyperspectral image fusion map characteristics

Abstract

The invention provides a wheat leaf layer nitrogen content estimation method based on hyperspectral image fusion map characteristics. The method comprises the following steps: acquiring wheat canopy hyperspectral image data and actually measured wheat leaf layer nitrogen content; firstly, performing image preprocessing, extracting spectral reflectance, calculating vegetation indexes, positions andshape features, and extracting deep features by using a convolutional neural network; secondly, performing feature optimization through correlation coefficient analysis and a random forest algorithm,and constructing new fusion map features by using a parallel fusion strategy; finally, constructing a wheat leaf layer nitrogen content estimation model based on fusion map characteristics by utilizing a particle swarm optimization support vector regression method. The method is high in estimation precision, high in feature robustness and suitable for the whole growth period of wheat, and meanwhile, the method for estimating the nitrogen content of the wheat leaf layer by integrating the vegetation index, the position and shape features and the deep features of the hyperspectral image to construct fusion map features is proposed for the first time at present.

Description

technical field [0001] The invention belongs to the field of crop growth monitoring, in particular to a method for estimating nitrogen content in wheat leaf layers based on hyperspectral image fusion map features. Background technique [0002] As an important food crop in China, wheat plays an important role in agricultural production and strategic grain reserves. Nitrogen is an important nutrient element in the growth period of wheat and an important basis for determining the quality and yield of wheat. Quantitative monitoring of nitrogen has become an important research direction in the field of agricultural remote sensing, and is the key to crop growth monitoring, precise farming management and precise fertilization in the development of smart agriculture. Hyperspectral imagery monitoring, in particular, provides not only spatial and spectral information about the reflectivity of vegetation canopies, but also rich spatial and locational features. Therefore, crop growth ...

AI Technical Summary

Problems solved by technology

However, few studies have used deep learning methods to extract deep features of canopy hyperspectral images to quantitatively estimate the nitrogen content of wheat leaves. In addition, traditional map features are not ideal for monitoring under high nitrogen levels, and are prone to saturation, which leads to the estimation model overfitting

Images