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Soil nutrient prediction and comprehensive evaluation method based on machine learning algorithm

A soil nutrient and comprehensive evaluation technology, applied in the field of soil nutrient prediction and comprehensive evaluation based on machine learning algorithm, can solve the problems of long time and heavy field investigation workload.

Pending Publication Date: 2019-02-22
NORTHWEST UNIV(CN)
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] To sum up, the problems existing in the existing technology are: the soil resource survey in the prior art requires the support of large-capacity samples to obtain relatively accurate results, and the field survey workload is heavy and time-consuming

Method used

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  • Soil nutrient prediction and comprehensive evaluation method based on machine learning algorithm
  • Soil nutrient prediction and comprehensive evaluation method based on machine learning algorithm
  • Soil nutrient prediction and comprehensive evaluation method based on machine learning algorithm

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Experimental program
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Effect test

Embodiment 1

[0112] 1 Soil sample collection and determination

[0113] In accordance with the unified technical specifications and requirements of the Ministry of Agriculture for soil testing and formula fertilization, following the principles of comprehensiveness, balance and objectivity, referring to the soil map and land use status map of each county, combined with remote sensing images, the sample point distribution map was drawn and the sampling unit was divided. A representative plot was selected for each sampling unit, and the sampling depth was determined according to the type of crops (0-20 cm for field crops such as wheat and corn, and 0-40 cm for orchards such as kiwi and apple). Take 8 points at random, use GPS to determine the latitude, longitude and altitude of the sample points, mix the soil at each sampling point and use the quartering method to retain 1kg soil samples and pack them into bags. The samples are divided into fresh samples and air-dried samples in the laborator...

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Abstract

The invention belongs to the technical field of soil detection, and discloses a soil nutrient prediction and comprehensive evaluation method based on machine learning algorithm. The method includes collecting a soil sample and measuring various soil nutrient indexes and soil moisture; collecting various environmental variable data; conducting spatial interpolation on each soil nutrient index for prediction by combining correlation analysis with random forests to determine the spatial distribution of soil nutrients; comparing the prediction accuracy of a model by calculating an average error, amean absolute error and a root mean square error of a verification point; determining the correlation between the soil nutrient and the soil moisture and between environmental variables and fertilization amount; utilizing a projection seeking model to comprehensively evaluate the soil nutrient and making a spatial distribution map of evaluation grade results. The method attempts to provide a newidea for soil nutrient evaluation from a non-linear perspective by the relationship between nutrient grades and evaluation indexes.

Description

technical field [0001] The invention belongs to the technical field of soil detection, and in particular relates to a soil nutrient prediction and comprehensive evaluation method based on a machine learning algorithm. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Soil nutrients are the basis of land productivity, a necessary condition for crop growth, a key factor affecting crop yield and quality, and have a strong impact on land use and ecological processes. Soil organic matter is not only a nutrient pool for crops, but also provides energy for the life of soil microorganisms, and plays a decisive role in regulating soil water, fertilizer, and heat conditions, and maintaining good soil physical properties; nitrogen, phosphorus, and potassium in soil are Necessary for plant growth, nitrogen and phosphorus are directly involved in the synthesis and conversion of proteins, nucleic acids, chlorophyll ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/24G06N3/04G06N3/08G06K9/62
CPCG06N3/086G01N33/24G06N3/048G06F18/2414
Inventor 杨联安任丽
Owner NORTHWEST UNIV(CN)
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