Development of Smart Weighing Lysimeter for Measuring Evapotranspiration and Developing Crop Coefficient for Greenhouse Chrysanthemum

Sagar, Atish; Hasan, Muzaffar; Singh, Dhirendra Kumar; Al‐Ansari, Nadhir; Chakraborty, Debashis; Singh, Mandeep; Iquebal, Mir Asif; Kumar, Amit; Malkani, Pankaj; Vishwakarma, Dinesh Kumar; Elbeltagi, Ahmed

doi:10.3390/s22166239

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…For PBIAS, between 0 to 10 is considered to be very good, 10 to 15 is considered to be good, 15 to 25 is considered to be fair and more than 25 is regarded as unsatisfactory or inadequate. A better and excellence model is one that has the minimum value of root mean square error (RMSE) and percent bias (PBIAS) and higher values of coefficients of correlation (CC) [41][42][43][44][45][46].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

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In this study, four different soft computing AI techniques were tested for the prediction of sediment yield based on hydro-meteorological variables at Jondhara station, Seonath stream in Rajnandgaon district, India. In order to fulfill this purpose, the models namely, multilayer perceptron (MLP), support vector machine (SVM), multilayer perceptron coupled with genetic algorithm (MLP-GA), and support vector machine coupled with genetic algorithm (SVM-GA) models were employed. To select the optimal input variables, a statistical method such as the Gamma test was considered among several methods. Based on the results of the analysis, all models were evaluated by using the following statistical indices: Coefficient of Correlation (CC), room mean square error (RMSE) and percent bias (PBAIS). Overall, the performance of the studied models indicates that all of them are capable of simulation sediments yield at Jondhara station, Seonath river basin in a satisfactory manner. Comparison of results showed that the MLP-GA with CC = 0.988, RMSE = 0.006 and PBIAS = 0.000 in training period and CC= 0.990, RMSE = 0.007 and PBIAS = 0.000 in testing period for S-6 model and CC = 0.986, RMSE = 0.025 and PBIAS = -0.001 in training period and CC = 0.988, RMSE = 0.029 and PBIAS = -0.001 in testing period for S-13 model were able to yield better results than the other models considered. Furthermore, an SVM model is also observed to have some advantages over MLP models and SVM-GA models since it can represent the output data in a continuous manner by fitting a linear regression function to the output data, which has the advantage of making the model more precise than MLP and SVM-GA models.

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Section: Model Performance Evaluationmentioning

confidence: 99%

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Yadav

Alam²,

Suryavanshi³

2023

IJECC

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“…Eddy covariance (EC) techniques are popular because they provide direct meteorological observations of wind speed, relative humidity, and radiant fluxes that can be used to calculate bare soil evaporation; however, this approach is not appropriate for many applications due to the inherent issues that arise as a result of the spatial averaging area (extending several kilometers up fetch from the EC station). Water balance weighing lysimeters have been used for many years to measure evaporation, evapotranspiration [8], water stress Sensors 2024, 24, 2039 2 of 19 in plants [9], crop-specific water use [10,11], and groundwater recharge [12], and they are commonly used to calibrate EC stations [13]. Weighing lysimeters therefore present a viable, high-precision alternative solution to compute bare soil evaporation.…”

Section: Introductionmentioning

confidence: 99%

Closing the Water Balance with a Precision Small-Scale Field Lysimeter

Lyles,

Sion,

Page

et al. 2024

Sensors

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We developed a set of two precision, small-scale, water balance lysimeters to provide accurate measurements of bare soil evaporation. Each lysimeter comprises a soil tank, a balance assembly with load cell, a wicking drainage system, and a stilling well to measure drained water. Fiberglass wicks installed at the bottom of the soil tanks provide −60 cm of tension to the base of the soil column, and soil water drainage is quantified to close the water balance within the lysimeter. The calibrated lysimeters return mass changes with uncertainties ranging from 3 to 8 g, corresponding to uncertainties of 0.02–0.05 mm of water. Installed at a semi-arid site in northern Nevada, the two lysimeters are filled with uniform construction sand and silt loam. Over a six-month pilot observation period, bare soil evaporation rates of 0.19 and 0.40 mm/day were measured for the construction sand and silt loam, respectively, which is consistent with meteorological data and models of potential evapotranspiration at the site. The design of the lysimeter can be adapted to specific research goals or site restrictions, and these instruments can contribute significantly to our ability to close the soil water balance.

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A review of lysimeters from the perspective of measurement performance and intelligent development in China

Gao,

Yang,

et al. 2024

Irrigation and Drainage

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Lysimeters are critical instruments for studying infiltration, runoff and evapotranspiration processes in the hydrological cycle and play a critical role in promoting efficient water‐saving irrigation. This paper provides a systematic review of the development process and research status of lysimeters, progressing from simple to complex designs, from single instruments to clusters, and from manual measurement to intelligent monitoring. The working principles of various types of lysimeter were first described based on Chinese lysimeter patents over the past 20 years. Second, this paper analyses the measurement performance and application scope of different lysimeters and further studies the weighting stability of various weighing lysimeters. Third, by analysing sources of measurement error, the reliability improvement methods of lysimeters are analysed from the aspects of environment control and sensor compensation. Fourth, this paper reviews typical research on the automation and informatization of lysimeters and discusses future intelligent developments and application prospects for lysimeters in complex environments. Finally, the measurement performance of different lysimeters is analysed based on experimental data sets. This paper puts forward some suggestions for developing lysimeters with high performance, high efficiency and high reliability, hoping to promote the development of intelligent agriculture.

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Development of Smart Weighing Lysimeter for Measuring Evapotranspiration and Developing Crop Coefficient for Greenhouse Chrysanthemum

Cited by 8 publications

References 32 publications

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Daily Sediment Yield Prediction Using Hybrid Machine Learning Approach

Closing the Water Balance with a Precision Small-Scale Field Lysimeter

A review of lysimeters from the perspective of measurement performance and intelligent development in China

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