AU2020102098A4 - Soil salinity degradation estimation by regression algorithm using agricultural internet of things - Google Patents

Soil salinity degradation estimation by regression algorithm using agricultural internet of things Download PDF

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AU2020102098A4
AU2020102098A4 AU2020102098A AU2020102098A AU2020102098A4 AU 2020102098 A4 AU2020102098 A4 AU 2020102098A4 AU 2020102098 A AU2020102098 A AU 2020102098A AU 2020102098 A AU2020102098 A AU 2020102098A AU 2020102098 A4 AU2020102098 A4 AU 2020102098A4
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soil salinity
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Deepesh Kumar Gautam
Mohammad Rafique Khan
Nitesh Kumar Sharma
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Abstract

SOIL SALINITY DEGRADATION ESTIMATION BY REGRESSION ALGORITHM USING AGRICULTURAL INTERNET OF THINGS ABSTRACT Salinization of the soil has an effect on agricultural development and nutrition protection. Soil salinity is a tendency of soil depletion which has a significant effect on agricultural manufacturing. Agricultural Internet of Things (AloT) endorsed approach is recommended to assess the degree of soil salinity and environmental factors to prescribe water resources, with the objective of draining salts from the base region of grains in soil salinity. The posterior processing enables measurement of several soil and crop characteristics in a depth manner. These comprise compact X-ray, spectroscopy, digital camera, tablet, checking for multistripe laser triangulation, ground-penetrating radar and scanner for electromagnetism. The Agricultural Internet of Things (AloT) and Machine Learning (ML) are focused on the approximation of the desorption water criteria for saline soils leveraging in-situ analysis of the salt concentration and the temperature in the agricultural land. Sensor devices for water, soil and crop leverage modern technology to promote agricultural production, allow agricultural customers, reduce and conserve input production costs, manage natural resource smartly, and enhance income and competitiveness. This proposal approaches the regression algorithm called Random forest regression (RFR) algorithms which is implemented to the consolidated datasets. RFR is developed by an aggregate of increasing decision trees based on random vectors and needs to start with various bootstrap datasets which are derived arbitrarily from the initial training sample. A primary mechanism in RFR is to employ Bagging in tandem with random extraction of characteristics, since Bagging can significantly mitigate the variance of unpredictable processes including tree formation, resulting in enhanced forecasting and increased accuracy. The proposal demonstrates that the random forest regression model is reliable for estimating degradation of the soil salinity and predicts the salinity in soil with high accuracy. 1 P a g e

Description

SOIL SALINITY DEGRADATION ESTIMATION BY REGRESSION ALGORITHM USING AGRICULTURAL INTERNET OF THINGS
Description
Field of the Invention.
The field of the invention is related to the Internet of Things.
The advancement of smart technology with the internet of things in various domain, mainly ease the techniques deployed. Mostly, this invention is soil salinity degradation estimation by the regression algorithm using the Agricultural internet of things. AloT endorsed approach is recommended to assess the degree of soil salinity and environmental factors to prescribe water resources, to drain salts from the base region of grains in soil salinity. The machine learning enhances the forecasting with increased accuracy deploying Random forest regression.
Background of the invention.
In recent, soil salinity degradation has become an essential factor to be estimated to prevent individual calamities like soil erosion that will degrade the agriculture development affecting the green environment and irrigation. Salinization of the soil also has an effect on and nutrition protection. Soil salinity is a tendency of soil depletion, which has a significant effect on agricultural manufacturing.
Initially, there were several laboratory tests with chemicals that were conducted to estimate the soil salinity. But it is prone to inaccurate sometimes. Even the cost of deploying the devices for measurement and performing the test is more and takes a long period to complete the test. So there needs a smart technology with the internet of things in
1 Pa g e agriculture to estimate the soil salinity degradation to make ease of the use with good results.
The Agricultural Internet of Things (AloT) endorsed approach is recommended to assess the degree of soil salinity and environmental factors to prescribe water resources, to drain salts from the base region of grains in soil salinity. The posterior processing enables the measurement of several soil and crop characteristics in a depth manner. These comprise compact X-ray, spectroscopy, digital camera, tablet, checking for multi-stripe laser triangulation, ground-penetrating radar, and scanner for electromagnetism.
In recent years, there is sudden, and often climate change occurs in the world. So there is always a need for unmanned remote sensors to capture the parameters to estimate the soil salinity.
Even graphical pictures taken from the satellite can be deployed for analyzing the soil salinity. Still, it does not give accurate results since it gives the image of the surface of the soil alone.
The training datasets are obtained from different sensor devices for water, soil, and crop leverage modern technology to promote agricultural production, allow agricultural customers, reduce and conserve input production costs, manage natural resources smartly, and enhance income and competitiveness.
A cloud is deployed for storing a large volume of datasets through the gateway. The machine learning Random forest regression (RFR) is developed by an aggregate of increasing decision trees based on random vectors and needs to start with various bootstrap datasets that are derived arbitrarily from the initial training sample. A primary mechanism in RFR is to employ bagging, which can significantly mitigate the variance of unpredictable processes, including tree formation, resulting in enhanced forecasting and increased accuracy.
2|Page
The Agricultural Internet of Things (AloT) with Machine Learning (ML) deployed to provide the approximation of the desorption water criteria for saline soils, leveraging in situ analysis of the salt concentration and the temperature in the agricultural land. The predictions assist in estimation and to take necessary precautions to handle the soil salinity.
Objects of the Invention
The main object of the invention is to deploy a soil salinity degradation estimation by a regression algorithm using the agricultural internet of things. Salinity may affect the surface protection of the soil and may degrade the ability of the soil for agriculture. The Agricultural Internet of Things (AloT) with Machine Learning (ML) regression analysis was deployed to provide the approximation of the desorption water criteria for saline soils, leveraging in-situ analysis of the salt concentration and the temperature in the agricultural land. The machine learning also enhances the forecasting with increased accuracy deploying Random forest regression. The predictions assist in taking necessary precautions to handle the soil salinity.
Summary of the Invention
Salination is the essential factor that needs to be estimated in the field of agriculture growth as it affects the soil. Though there was a manual test performed to estimate the soil salinity degradation, it was inaccurate sometimes, costlier, and takes a long time to complete the estimation. Even the image analysis also does not give enough information to estimate the salinity since they only provide surface information. So an electrical conductivity sensor, temperature, and humidity sensors are used to gather the datasets to be analyzed and make predictions. The datasets are sent to the large storage space cloud through the gateway. The training set with the test data analyzed with machine learning random forest regression. The regression analysis that is implemented on the consolidated datasets of the training set and the test set deploys bagging in tandem with random extraction of characteristics, since bagging can significantly mitigate the variance of unpredictable processes, including tree
3|Page formation, resulting in enhanced forecasting and increased accuracy. From the predictions of the meteorological data using a machine learning algorithm, the estimation is computed by Blaney-Criddle equation. The entire communication and computation are monitored using smart devices by the user. The results obtained enable us to take the necessary precautions to handle the soil salinity.
Detailed Description of the Invention
Fig. 1 shows the block diagram of the invention of soil salinity degradation estimation by machine learning using the agricultural internet of things(AIOT). Soil salinity is a tendency of soil depletion, which has a significant effect on agricultural manufacturing. Salination is the most crucial factor that needs to be estimated in the field of agriculture growth as it affects the soil, which in turn affects economic growth, which involves irrigation and food production. Though there were many manual tests performed to estimate the soil salinity degradation, it was inaccurate sometimes, costlier, and takes a long time to complete the estimation. Even the image analysis also does not give enough information to estimate the salinity since they only provide surface information. The surface cover cannot identify the characteristics of the soil. So an electrical conductivity sensor, temperature, and humidity sensors are used to gather the datasets to be analyzed and make predictions. The machine learning regression algorithm performs the computation of the training set with the test data. From the implementation of the consolidated datasets of the training set and the test set deploying regression algorithm with bagging, the estimation is computed by Blaney-Criddle equation. The entire communication and computation are monitored using smart devices by the user. The results obtained enable us to take the necessary precautions to handle the soil salinity.
Fig. 2 shows the process block diagram of the estimation of soil salinity degradation by the regression algorithm using the agricultural internet of things. Soil salinity degradation has become an essential factor to be estimated to prevent individual calamities like soil erosion that will degrade the agriculture development affecting the green environment and irrigation. Salinization of the soil also has an effect on
4|Page and nutrition protection. AloT endorsed approach is recommended to assess the degree of soil salinity and environmental factors to prescribe water resources, to drain salts from the base region of grains in soil salinity. In this process, electrical conductivity sensor, temperature, and humidity sensors are used to capture physical parameters, which are the datasets to be analyzed and make predictions. The datasets are sent to the large storage space cloud through the gateway. The training set with the test data analyzed with machine learning random forest regression. The regression analysis that is implemented on the consolidated datasets of the training set and the test set deploys bagging in tandem with random extraction of characteristics, since bagging can significantly mitigate the variance of unpredictable processes, including tree formation, resulting in enhanced forecasting and increased accuracy. From the predictions of the meteorological data using a machine learning algorithm, the estimation is computed by Blaney-Criddle equation. The entire communication and computation are visualized using smart devices by the user. The results obtained enable us to take the necessary precautions to handle the soil salinity.
|Page

Claims (6)

  1. SOIL SALINITY DEGRADATION ESTIMATION BY REGRESSION ALGORITHM USING AGRICULTURAL INTERNET OF THINGS
    CLAIMS:
    I/We Claim: 1. A highly configured electrical conductivity sensor and temperature and humidity sensor capture the meteorological data.
  2. 2. Highly configured computer carry out an estimation of Blaney-Criddle equation
  3. 3. A high-speed optic fiber connection performs the computing in software approaches like python.
  4. 4. A massive volume of cloud platform stores the datasets that are received through the gateway.
  5. 5. Machine learning Random forest regression analysis of the training set and the test set.
  6. 6. Smart devices monitor the communication and computation results and display them to the user.
    1 Pag e
    SOIL SALINITY DEGRADATION ESTIMATION BY REGRESSION 02 Sep 2020
    ALGORITHM USING AGRICULTURAL INTERNET OF THINGS
    Drawings 2020102098
    Fig.1 BLOCK DIAGRAM
    1|Page
    Fig.2 PROCESS FLOW DIAGRAM
    2|Page
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113009482A (en) * 2021-02-01 2021-06-22 中国科学院东北地理与农业生态研究所 Method for monitoring salt content of saline soil on ground surface under planting covering
CN113011372A (en) * 2021-04-01 2021-06-22 清华大学 Automatic monitoring and identifying method for saline-alkali soil
CN113435640A (en) * 2021-06-24 2021-09-24 中国科学院东北地理与农业生态研究所 Method for predicting in-situ EC (soil EC) of soil in different plough layers of main growth period of rice in soda saline-alkali soil
CN116310842A (en) * 2023-05-15 2023-06-23 菏泽市国土综合整治服务中心 Soil saline-alkali area identification and division method based on remote sensing image
CN117787666A (en) * 2024-02-26 2024-03-29 山东省国土空间生态修复中心(山东省地质灾害防治技术指导中心、山东省土地储备中心) Saline-alkali soil information monitoring and treatment method, system, equipment and storage medium

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113009482A (en) * 2021-02-01 2021-06-22 中国科学院东北地理与农业生态研究所 Method for monitoring salt content of saline soil on ground surface under planting covering
CN113011372A (en) * 2021-04-01 2021-06-22 清华大学 Automatic monitoring and identifying method for saline-alkali soil
CN113435640A (en) * 2021-06-24 2021-09-24 中国科学院东北地理与农业生态研究所 Method for predicting in-situ EC (soil EC) of soil in different plough layers of main growth period of rice in soda saline-alkali soil
CN113435640B (en) * 2021-06-24 2022-07-26 中国科学院东北地理与农业生态研究所 Method for predicting in-situ EC (environmental impact) of soils with different plough layers in main growth period of rice in soda saline-alkali soil
CN116310842A (en) * 2023-05-15 2023-06-23 菏泽市国土综合整治服务中心 Soil saline-alkali area identification and division method based on remote sensing image
CN117787666A (en) * 2024-02-26 2024-03-29 山东省国土空间生态修复中心(山东省地质灾害防治技术指导中心、山东省土地储备中心) Saline-alkali soil information monitoring and treatment method, system, equipment and storage medium
CN117787666B (en) * 2024-02-26 2024-05-28 山东省国土空间生态修复中心(山东省地质灾害防治技术指导中心、山东省土地储备中心) Saline-alkali soil information monitoring and treatment method, system, equipment and storage medium

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