Wheat leaf layer nitrogen content estimation method based on RGB image fusion features

An RGB image and fusion feature technology, which is applied in the field of nitrogen content estimation in wheat leaves based on RGB image fusion features, can solve the problem that shallow neural network learning cannot express deep features.

Pending Publication Date: 2021-04-06
NANJING AGRICULTURAL UNIVERSITY
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AI Technical Summary

Problems solved by technology

Although wavelet texture features can make up for the spatial characteristics of wheat canopy, learning based on shallow neural networks still cannot express more deep features

Method used

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  • Wheat leaf layer nitrogen content estimation method based on RGB image fusion features
  • Wheat leaf layer nitrogen content estimation method based on RGB image fusion features
  • Wheat leaf layer nitrogen content estimation method based on RGB image fusion features

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Experimental program
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Embodiment 1

[0085] The present invention is based on wheat field experiments with different growth stages, different nitrogen application levels, and different planting densities, and the specific expressions are shown in Table 1 and Table 2.

[0086] Table 1 Basic information of wheat experimental fields

[0087]

[0088]

[0089] Table 2 Wheat canopy images and data collection of agronomic parameters

[0090]

[0091] Obtained wheat canopy UAV RGB image data from wheat experimental fields Exp.1 and Exp.2. The data acquisition is highly systematic, covers two main wheat varieties, includes main growth periods, and has a large number of samples and many processing factors. Effectively verify the accuracy and adaptability of the identification method of the present invention under different environmental conditions and treatments.

[0092] Estimation method of wheat leaf nitrogen content based on RGB image fusion features, the specific steps are as follows:

[0093] Step 1. Data...

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Abstract

The invention provides a wheat leaf layer nitrogen content estimation method based on RGB image fusion features. The method comprises the following steps that a wheat canopy RGB image and actually-measured wheat leaf layer nitrogen content are collected; RGB image preprocessing is carried out, and a visible light vegetation index is calculated; multi-scale wavelet texture feature extraction in the horizontal direction, the vertical direction and the diagonal direction is achieved through a discrete wavelet transformation method; deep features of the RGB image are extracted by using a convolutional neural network; and finally, a particle swarm optimization support vector regression model based on fusion features is constructed to estimate the nitrogen content of the wheat leaf layer. The method is high in estimation precision, high in feature robustness and suitable for the whole growth period of wheat, and meanwhile the visible light vegetation index, the wavelet texture feature and the optimized deep feature of the comprehensive RGB image are proposed for the first time at present to construct fusion features to estimate the nitrogen content of the wheat leaf layer.

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 RGB image fusion features. Background technique [0002] As an important food crop in China, wheat plays an important role in agricultural production and strategic grain reserves. Nitrogen (N) is one of the most basic nutrients required for wheat growth, and nondestructive remote sensing monitoring of nitrogen content is of great significance for efficient management of wheat fields. 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. [0003] Remote sensing (RS) technology has become an important tool for real-time and non-destructive estimation of crop nitrogen (Nitrogen, N) status, providing a sci...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/00G01N21/17G01N21/84
CPCG06N3/006G01N21/84G01N21/17G01N2021/8466G01N2021/1793G06V20/188G06V10/44G06V10/56G06F18/2411G06F18/253
Inventor 朱艳杨宝华姚霞马吉峰郑恒彪曹卫星田永超程涛邱小雷张羽
Owner NANJING AGRICULTURAL UNIVERSITY
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