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

A hyperspectral image and plant nitrogen content technology, applied in the field of wheat plant nitrogen content estimation, can solve problems such as ignoring and weakening spatial spectral structure

Pending Publication Date: 2021-01-29
ANHUI AGRICULTURAL UNIVERSITY
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Problems solved by technology

However, due to the deep neural network structure, the convolutional neural network usually also wea

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

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Embodiment Construction

[0082] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0083] The present invention is based on wheat field experiments with different growth stages, different nitrogen application levels, and different planting densities, as shown in Table 1 and Table 2 specifically.

[0084] Table 1 Basic information of wheat experimental fields

[0085]

[0086] Table 2 Wheat canopy images and data collection of agronomic parameters

[0087]

[0088] Obtain wheat canopy hyperspectral image data from the wheat experimental field Exp.1, the data acquisition is strong, covers two main wheat varieties, in...

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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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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/40G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/30G06V10/25G06V10/40G06N3/045G06F18/25
Inventor 杨宝华刘碧云黄正来武立权张海鹏朱月
Owner ANHUI AGRICULTURAL UNIVERSITY
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