2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 2 , Issue 2 , PP: 77-83, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Sleem 1 * , Ibrahim Elhenawy 2
Doi: https://doi.org/10.54216/JISIoT.020204
Water scarcity is a significant issue in agriculture, making efficient irrigation practices crucial for sustainable farming. Integration of Internet of Things (IoT) and machine learning technologies are becoming of great importance to improve irrigation efficiency and reduce water usage. In this paper, we propose an intelligent irrigation system that take the advantage of IoT to improve the predictive analytics of groundwater levels. Our system used a deep learning to estimate the groundwater level using convolutional recurrent model that analyzed the sensory measurements necessary to predict groundwater levels. The model is trained on a large dataset of time series records and corresponding groundwater levels, allowing it to learn the complex patterns and relationships between time series features and groundwater levels. The experimental predictive analytics provided accurate irrigation recommendations, and the remote monitoring capabilities allowed farmers to adjust the irrigation schedule as needed.
Smart Irrigation System , Intelligent systems , Predictive Analytics , Deep Learning
[1]. Ferrández-Pastor, F. J., García-Chamizo, J. M., Nieto-Hidalgo, M., & Mora-Martínez, J. (2018). Precision agriculture design method using a distributed computing architecture on internet of things context. Sensors, 18(6), 1731.
[2]. Munir, M. S., Bajwa, I. S., Naeem, M. A., & Ramzan, B. (2018). Design and implementation of an IoT system for smart energy consumption and smart irrigation in tunnel farming. Energies, 11(12), 3427.
[3]. Chang, C. L., & Lin, K. M. (2018). Smart agricultural machine with a computer vision-based weeding and variable-rate irrigation scheme. Robotics, 7(3), 38.
[4]. Jayaraman, P. P., Yavari, A., Georgakopoulos, D., Morshed, A., & Zaslavsky, A. (2016). Internet of things platform for smart farming: Experiences and lessons learnt. Sensors, 16(11), 1884.
[5]. Zhang, C., Yue, P., Di, L., & Wu, Z. (2018). Automatic identification of center pivot irrigation systems from landsat images using convolutional neural networks. Agriculture, 8(10), 147.
[6]. Cambra, C., Sendra, S., Lloret, J., & Lacuesta, R. (2018). Smart system for bicarbonate control in irrigation for hydroponic precision farming. Sensors, 18(5), 1333.
[7]. Wu, R. S., Liu, J. S., Chang, S. Y., & Hussain, F. (2017). Modeling of mixed crop field water demand and a smart irrigation system. Water, 9(11), 885.
[8]. Mango, N., Makate, C., Tamene, L., Mponela, P., & Ndengu, G. (2018). Adoption of small-scale irrigation farming as a climate-smart agriculture practice and its influence on household income in the Chinyanja Triangle, Southern Africa. Land, 7(2), 49.
[9]. Işık, M. F., Sönmez, Y., Yılmaz, C., Özdemir, V., & Yılmaz, E. N. (2017). Precision irrigation system (PIS) using sensor network technology integrated with IOS/Android application. Applied Sciences, 7(9), 891.
[10]. Ampatzidis, Y., De Bellis, L., & Luvisi, A. (2017). iPathology: robotic applications and management of plants and plant diseases. Sustainability, 9(6), 1010.
[11]. Vellidis, G., Ortiz, B., Beasley, J., Hill, R., Henry, H., & Brannen, H. (2014). Reducing digging losses by using automated steering to plant and invert peanuts. Agronomy, 4(3), 337-348.
[12]. Wang, X., Shao, J., Steenbergen, F. V., & Zhang, Q. (2017). Implementing the prepaid smart meter system for irrigated groundwater production in northern China: Status and problems. Water, 9(6), 379.
[13]. Villarrubia, G., De Paz, J. F., De La Iglesia, D. H., & Bajo, J. (2017). Combining multi-agent systems and wireless sensor networks for monitoring crop irrigation. Sensors, 17(8), 1775.
[14]. Suh, D. H., Khachatryan, H., Rihn, A., & Dukes, M. (2017). Relating knowledge and percep tions of sustainable water management to preferences for smart irrigation technology. Sustainability, 9(4), 607.
[15]. Kim, S., Lee, M., & Shin, C. (2018). IoT-based strawberry disease prediction system for smart farming. Sensors, 18(11), 4051.
[16]. Ojha, C., Shirzaei, M., Werth, S., Argus, D. F., & Farr, T. G. (2018). Sustained groundwater loss in California's Central Valley exacerbated by intense drought periods. Water Resources Research, 54(7), 4449-4460.
