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

Abstract

The invention provides a wheat plant nitrogen content estimation method based on hyperspectral image fusion map characteristics. The method comprises the following steps: acquiring a wheat canopy hyperspectral image and the nitrogen content of a ground wheat plant; firstly, spectral reflectance is extracted, and vegetation indexes, positions and shape features are calculated; secondly, extractinga principal component hyperspectral image and extracting deep features by using a convolutional neural network; a random forest algorithm and a correlation coefficient analysis method are used again to determine optimal features, and a parallel fusion strategy is used to construct new fusion map features for the optimal features; and finally, establishing a support vector regression model based onthe fusion map characteristics to predict the nitrogen content of the wheat plants. The method is high in estimation precision, strong in model generalization and suitable for the whole growth periodof wheat, and is also a method for estimating the nitrogen content of the wheat plant by integrating the vegetation index, the position and shape characteristics and the deep characteristics of the hyperspectral image to construct fusion map characteristics for the first time at present.

Description

technical field [0001] The invention belongs to the technical field of crop growth monitoring, in particular to a method for estimating nitrogen content of wheat plants 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 the main nutrient that affects the growth and fertility, material productivity and yield quality of crops. Intelligent monitoring, quantitative diagnosis and dynamic regulation of crop nitrogen nutrition are the core content and scientific basis of precision agriculture. Accurate, rapid, and non-destructive monitoring of nitrogen is the key to improving crop productivity and competitiveness. Optimizing crop nitrogen management is crucial for precise intensification of agricultural production, food security, and sustainable agricultural development. Hyperspectral image monitoring, in particular, p...

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Problems solved by technology

However, due to the deep neural network structure, the convolutional neural network usually also weakens the spatial spectral structure, while ignoring a large amount of implicitly useful information.

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