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Convolutional neural network-based soil nitrogen content inversion model construction method and system
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A convolutional neural network and inversion model technology, applied in the field of soil nitrogen content inversion model construction based on convolutional neural network
Active Publication Date: 2022-05-24
JIANGXI AGRICULTURAL UNIVERSITY
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[0005] Based on this, the object of the present invention is to provide a method and system for constructing a soil nitrogen content inversion model based on a convolutional neural network, which is used to solve the problem of how to use remote sensing image data to carry out spatial mapping of soil total nitrogen in southern mountainous and hilly areas. technical problem
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Embodiment 1
[0043] see image 3 , which shows the construction method of the soil nitrogen content inversion model based on the convolutional neural network in the first embodiment of the present invention, and the method includes steps S101-S103:
[0044] S101. Obtain model training data, the model training data includes remote sensing image data and field soil samples, obtain remote sensing image range and multiple land use type samples according to the remote sensing image data, and combine the land use type samples with a convolutional neural network model for model training to obtain Obtain the first target model.
[0045] In the above steps, the land use type samples are combined with the convolutional neural network model for model training; the trained convolutional neural network model is subjected to accuracy evaluation in combination with the accuracy evaluation index to obtain the first target model, and the accuracy evaluation index includes the overall classification accurac...
Embodiment 2
[0056] Please check Figure 4 , which shows the construction method of the soil nitrogen content inversion model based on the convolutional neural network in the second embodiment of the present invention, and the method includes steps S201-S203:
[0057] S201. Obtain model training data, where the model training data includes remote sensing image data and field soil samples, obtain the remote sensing image range and multiple land use type samples according to the remote sensing image data, and combine the land use type samples with a convolutional neural network model for model training to obtain Obtain the first target model.
[0058] The data collection stage includes remote sensing image data, related map data and field soilsample collection. After being air-dried, ground and sieved in the laboratory, the soil samples were evenly divided into two parts, one part was used to determine the total nitrogen content of the soil, and the other part was used to obtain soil spect...
Embodiment 3
[0078] see Figure 5 , which shows the construction system of the soil nitrogen content inversion model based on the convolutional neural network in the third embodiment of the present invention, and the system includes:
[0079] an acquisition module for acquiring model training data, the model training data including remote sensing image data and field soil samples, acquiring remote sensing image ranges and multiple land use type samples according to the remote sensing image data, and combining the land use type samples The convolutional neural network model performs model training to obtain the first target model;
[0080] A conversion module, configured to obtain a land use type spatial distribution map according to the first target model in combination with the remote sensing image range, obtain a land use classification result according to the land use type spatial distribution map, and analyze different land use classification results according to the land use classific...
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Abstract
The invention provides a soil nitrogen content inversion model construction method and system based on a convolutional neural network, and the method comprises the steps: obtaining remote sensing image data to obtain a remote sensing image range and a land utilization type sample, and carrying out the model training of the land utilization type sample, and obtaining a first target model; combining the first target model with a remote sensing image range to obtain a land utilization type spatial distribution map, and converting an image spectrum into an indoor spectrum; the method comprises the following steps: acquiring soil spectrum data, performing spectrum pretreatment on the soil spectrum data to obtain the soil total nitrogen content, establishing a spectrum and soil total nitrogen content target model by combining with an indoor spectrum, and constructing a soil total nitrogen content spatial distribution diagram by combining with a remote sensing image range. According to the soil nitrogen content inversion model construction method based on the convolutional neural network, the image spectrum is converted into the indoor spectrum, so that soil total nitrogenspatial mapping of southern mountainous and hilly areas is realized, and the spatial mapping precision of soil total nitrogen in vegetation coverage areas is improved.
Description
technical field [0001] The invention relates to the technical field of cropgrowth monitoring, in particular to a method and system for constructing a soil nitrogen content inversion model based on a convolutional neural network. Background technique [0002] With the rapid development of satellite remote sensing at home and abroad, the variety of satellite images of multispectral data and hyperspectral data has increased, which provides effective data support for the research on the inversion of soil alkaline hydrolyzable nitrogen content based on multispectral and hyperspectral data. [0003] As an essential nutrient in soil, soil nitrogen plays an important role in the process of plant growth and development, and its content has an important impact on crop yield and quality. Hyperspectral remote sensing technology has developed rapidly with its advantages of high spectral resolution and rich band information. It has strong advantages in quickly estimating soil and crop in...
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