Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure-Reference-Cited by-同舟云学术

Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure

Published:2021-09-27 Issue: Volume: Page:
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Bülbül Mehmet Akif^ORCID,Öztürk Celal

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-021-06168-4.pdf

Reference44 articles.

1. Işık, M.; Sönmez, Y.; Yılmaz, C.; Özdemir, V.; Yılmaz, E.: Precision irrigation system (PIS) using sensor network technology integrated with IOS/android application. Appl. Sci. 7, 891 (2017). https://doi.org/10.3390/app7090891

2. Bjornlund, H.; van Rooyen, A.; Pittock, J.; Parry, K.; Moyo, M.; Mdemu, M.; de Sousa, W.: Institutional innovation and smart water management technologies in small-scale irrigation schemes in southern Africa. Water Int. (2020). https://doi.org/10.1080/02508060.2020.1804715

3. Kumar, S.: Reference evapotranspiration (ETo) and irrigation water requirement of different crops in Bihar. J. Agrometeorol. 19(3), 238–241 (2017)

4. Car, N.J.; Christen, E.W.; Hornbuckle, J.W.; Moore, G.A.: Using a mobile phone Short Messaging Service (SMS) for irrigation scheduling in Australia: farmers’ participation and utility evaluation. Comput. Electron. Agric. (2012). https://doi.org/10.1016/j.compag.2012.03.003

5. Lajara, R.; Alberola, J.; Pelegrí-Sebastiá, J.: A solar energy powered autonomous wireless actuator node for irrigation systems. Sensors (2011). https://doi.org/10.3390/s110100329

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Smart Irrigation for Sustainable Farming: Low- Cost IoT Solution;2024-08-07

2. A novel hybrid deep learning model for early stage diabetes risk prediction;The Journal of Supercomputing;2024-05-27

3. Optimizing Hyperparameters for Enhanced Performance in Convolutional Neural Networks: A Study Using NASNetMobile and DenseNet201 Models;Mühendislik Bilimleri ve Araştırmaları Dergisi;2024-04-30

4. Artificial Intelligence for Water Consumption Assessment: State of the Art Review;Water Resources Management;2024-04-25

5. Feature Selection and Hyperparameters Optimization Employing a Hybrid Model Based on Genetic Algorithm and Artificial Neural Network: Forecasting Dividend Payout Ratio;Computational Economics;2024-01-05