Towards a Smart Irrigation Scheduling System Through Massive Data and Predictive Models

2020 ◽  
pp. 375-384
Author(s):  
Asmae El Mezouari ◽  
Mehdi Najib ◽  
Abdelaziz El Fazziki
Keyword(s):  
Predictive Models ◽  
Irrigation Scheduling ◽  
Massive Data ◽  
Scheduling System

AgriSens: IoT-Based Dynamic Irrigation Scheduling System for Water Management of Irrigated Crops

2020 ◽  
pp. 1-1
Author(s):  
Sanku Kumar Roy ◽  
Sudip Misra ◽  
Narendra Singh Raghuwanshi ◽  
Sajal K. Das
Keyword(s):  
Water Management ◽  
Irrigation Scheduling ◽  
Scheduling System ◽  
Irrigated Crops

Automatic Irrigation Scheduling System (AISSUM): Principles and Applications

1995 ◽  
Vol 121 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Bhawan Singh ◽  
Jean Boivin ◽  
Glenn Kirkpatrick ◽  
Barry Hum
Keyword(s):  
Irrigation Scheduling ◽  
Scheduling System

A Sensor and Actuator System for Evapotranspiration Based Irrigation Scheduling System

2021 ◽  
Author(s):  
Andi Hendra Putra Ganesha ◽  
Kevin Kinguantoro ◽  
Martinus Davin Herell ◽  
Wirenda Sekar Ayu ◽  
Gilang Mardian Kartiwa ◽  
...  
Keyword(s):  
Irrigation Scheduling ◽  
Scheduling System ◽  
Actuator System

Online professional irrigation scheduling system for greenhouse crops

2017 ◽  
pp. 221-228 ◽  
Author(s):  
N. Katsoulas ◽  
T. Bartzanas ◽  
C. Kittas
Keyword(s):  
Irrigation Scheduling ◽  
Scheduling System ◽  
Greenhouse Crops

scholarly journals Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3408 ◽  
Author(s):  
Olutobi Adeyemi ◽  
Ivan Grove ◽  
Sven Peets ◽  
Yuvraj Domun ◽  
Tomas Norton
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Irrigation Scheduling ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Rule Based ◽  
Scheduling System ◽  
Rule Based System

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

Assessing the Utility of Primed Acclimation for Improving Water Savings in Cotton using a Sensor-Based Irrigation Scheduling System

Crop Science ◽  
2017 ◽  
Vol 57 (4) ◽  
pp. 2117-2129 ◽  
Author(s):  
Calvin D. Meeks ◽  
John L. Snider ◽  
Wesley M. Porter ◽  
George Vellidis ◽  
Gary Hawkins ◽  
...  
Keyword(s):  
Irrigation Scheduling ◽  
Water Savings ◽  
Scheduling System

scholarly journals Atmometer – based irrigation scheduling system for drip – irrigated onion (Allium cepa L.)

2018 ◽  
Vol 192 ◽  
pp. 03039
Author(s):  
Cresan Joy Villaroman ◽  
Armando Espino ◽  
Jeffrey Lavarias ◽  
Victorino Taylan
Keyword(s):  
Crop Production ◽  
Water Productivity ◽  
Irrigation Scheduling ◽  
Crop Water ◽  
San Jose ◽  
Scheduling System ◽  
Irrigation Interval ◽  
Randomized Design ◽  
Allium Cepa L ◽  
Monteith Equation

This study was conducted to develop an atmometer-based irrigation scheduling system for drip-irrigated onion production. The study was conducted at San Agustin, San Jose City Nueva Ecija from November 2016 – March 2017. Three treatments composing of three replicates were considered in the research. Treatments 1 and 2 were based on the recorded atmometer readings with an irrigation interval of two days and five days respectively. Treatment 3 is a soil moisture-based irrigation scheduling with a management allowed deficit of 50 %. Calibration curved was obtained by comparing the atmometer readings with the estimated evapotranspiration using Modified Penman-Monteith equation. It was used in computing the crop water requirement for Treatments 1 and 2. The important parameters that used to answer the objective of the study such as plant height, crop yield, bulb weight, bulb diameter, water use and water productivity, were acquired during and after crop production. The statistical analysis used in the study was Analysis of Variance for Complete Randomized Design and paired T-test. Based on the result, Treatment 1 was highly useful in increasing water productivity without sacrificing the crop qualities.

scholarly journals Water-saving Effect by Rotational Irrigation Scheduling System (RISS) in the Dongwha Dam

2012 ◽  
Vol 54 (2) ◽  
pp. 147-156
Author(s):  
Tai-Cheol Kim ◽  
Duck-Young Moon ◽  
Jae-Myun Lee ◽  
Jong-Pil Moon
Keyword(s):  
Irrigation Scheduling ◽  
Water Saving ◽  
Scheduling System ◽  
Saving Effect
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