Valve response under influence of pH on an evapotranspiration model

Poyen, Faruk Bin; Ghosh, Apurna; Kundu, Palash Kumar; Sen, Kausik

doi:10.1109/cesys.2016.7889952

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…In this paper the measurement of evapo-transpiration equation as well as soil nature calculated depending upon some equations. This paper also constructs a simulation model through which the water level and the PH balance can be measured [5].…”

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Model to Estimate Soil Parameters using Wireless Sensor: An Efficient Way of Data Collection in Precision Agriculture

Jana,

Roy*

2019

IJITEE

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Environmental and field parameters have high impact on agricultural productivity. The environmental parameters include temperature, humidity, rainfall and wind direction etc. whereas field parameters include salinity, nutrient content, oxygen levels, soil type, soil PH and soil moisture etc. Among them, Soil pH and moisture are highly correlated which can affect soil salinity, nutrient level and soil conductivity. So, these two parameters need to be measure precisely to take management decisions. Till now the process which is applied to measure soil parameters are entirely depends on laboratory testing of soil sample. This process has some overhead like availability of laboratory, manpower and cost. To overcome these challenges, we developed a virgin sensor based automated data collection technique which maintains the agricultural productivity with sustainable development. But practically sensor-based data retain some error which defers from laboratory result. In this paper, we have developed a new efficient technique to reduce the error in calculating pH value using sensors. In this research work, ten different types of soils are tested in laboratory to get the actual (pH level in soil. These values are considered as the ground truth for the experiment. The pH values of all the ten types of soil are collected from the field using wireless sensor. Our proposed mathematical model reduces the error to 0.01 between collected values and ground truth values with back propagation method based on soil moisture, environmental temperature and humidity. Here, the proposed model is empirically tested by taking some real field data values.

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Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Model to Estimate Soil Parameters using Wireless Sensor: An Efficient Way of Data Collection in Precision Agriculture

Jana,

Roy*

2019

IJITEE

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“…The JH and MB methods have been successfully applied in humid and arid environments [32,48] but the main drawback of these equations is underestimation in humid areas [35] and overestimation in semi-arid areas [32].…”

Section: Reference Evapotranspiration (Et O ) Estimation Methodsmentioning

confidence: 99%

“…ET is commonly estimated indirectly from meteorological data [9,32,33] using a variety of different methods [34][35][36]. These methods can be grouped into three categories: (i) those based on energy balance and mass transfer concepts, often referred to as the combination equation or Penman-Monteith (PM) method [9]; (ii) those based on empirical relationships between ET o and temperature-(e.g., Thornthwaite [37] and Hargreaves and Samani (HS) [38]); and (iii) and radiation-based approach which utilise measured or estimated solar radiation flux density at the surface (e.g., Jensen and Haise [39]; McGuinness and Bordne [40]; and Priestley and Taylor [41]).…”

Section: Reference Evapotranspiration (Et O ) Estimation Methodsmentioning

confidence: 99%

Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data

2017

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Estimating daily evapotranspiration is challenging when ground observation data are not available or scarce. Remote sensing can be used to estimate the meteorological data necessary for calculating reference evapotranspiration ET o . Here, we assessed the accuracy of daily ET o-G (PM) was adopted as the main benchmark. HS underestimated ET o by 2%-3% (R 2 = 0.86 to 0.90; RMSE = 0.95 to 1.2 mm day −1 at different stations). JH and MB overestimated ET o by 8% to 40% (R 2 = 0.85 to 0.92; RMSE from 1.18 to 2.18 mm day −1 ). The annual average values of ET o estimated using RS data and ground-based data were similar to one another reflecting low bias in daily estimates. They ranged between 1153 and 1893 mm year −1 for ET o-G and between 1176 and 1859 mm year −1 for ET o-RS for the different stations. Our results suggest that ET o-RS (HS) can yield accurate and unbiased ET o estimates for semi-arid regions which can be usefully employed in water resources management.

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Evapotranspiration-based Irrigation System for Mustard Green Crop Cultivation using Public Weather Forecast

Cruz

Caya

Ballado

et al. 2020

2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC)

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Valve response under influence of pH on an evapotranspiration model

Cited by 3 publications

References 3 publications

A Mathematical Model to Estimate Soil Parameters using Wireless Sensor: An Efficient Way of Data Collection in Precision Agriculture

A Mathematical Model to Estimate Soil Parameters using Wireless Sensor: An Efficient Way of Data Collection in Precision Agriculture

Estimating Daily Reference Evapotranspiration in a Semi-Arid Region Using Remote Sensing Data

Evapotranspiration-based Irrigation System for Mustard Green Crop Cultivation using Public Weather Forecast

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