PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Managing water resources in urban areas is relatively expensive due to the costs of electricity and water distribution from wells and water companies. Therefore, water resource management for urban agricultural purposes needs to be made efficient, such as through smart irrigation technologies, one of which is the drip irrigation system that engages soil moisture sensors and the Internet of Things (IoT) to control the amount of distributed water. This study aims to apply and evaluate the performance of a drip irrigation system based on soil moisture sensors and IoT in urban agriculture. The results showed that the distribution uniformity in the system was identified at fair levels, with a Coefficient of Uniformity (CU) of 90.15% and 86.58%, respectively. Furthermore, our study also found that the IoT-assisted drip irrigation system that engaged a Deep Neural Networks (DNN) model to meet the water requirement led to better peanut yield than the irrigation system based on soil moisture as a control.

• Ali, A., Rehman, A. U., Almogren, A., Eldin, E. T., & Kaleem, M. (2022). Application of deep learning gated recurrent unit in hybrid shunt active power filter for power quality enhancement. Energies, 15(20), Article 7553. https://doi.org/10.3390/en15207553

• Al-Mefleh, N. K., Talozi, S., & Naser, K. A. (2021). Assessment of treated wastewater reuse in drip irrigation under different pressure conditions. Water, 13(8), 1–15. https://doi.org/10.3390/w13081033

• Bennett, D. R., & Harms, T. E. (2011). Crop yield and water requirement relationships for major irrigated crops in Southern Alberta. Canadian Water Resources Journal, 36(2), 159–170. https://doi.org/10.4296/cwrj3602853

• Chaer, M. S. I., Abdullah, S. H., & Priyati, A. (2016). Application of arduino microcontroller on drip irrigation system for mustard plant (Brassica juncea). Jurnal Ilmiah Rekayasa Pertanian Dan Biosistem, 4(2), 228–238.

• Darimani, H. S., Kpoda, N., Suleman, S. M., & Luut, A. (2021). Field performance evaluation of a small-scale drip irrigation system installed in the Upper West Region of Ghana. Computational Water, Energy, and Environmental Engineering, 10, 82–94. https://doi.org/10.4236/cweee.2021.102006

• Dong, W., Li, C., Hu, Q., Pan, F., Bhandari, J., & Sun, Z. (2020). Potential evapotranspiration reduction and its influence on crop yield in the North China Plain in 1961-2014. Advances in Meteorology, 2020(1), Article 3691421. https://doi.org/10.1155/2020/3691421

• Ferrarezi, R. S., Nogueira, T. A. R., & Zepeda, S. G. C. (2020). Performance of soil moisture sensors in Florida sandy soils. Water, 12(2), Article 358. https://doi.org/doi:10.3390/w12020358

• Gimpel, H., Graf-Drasch, V., Hawlitschek, F., & Neumeier, K. (2021). Designing smart and sustainable irrigation: A case study. Journal of Cleaner Production, 315, Article 128048. https://doi.org/10.1016/j.jclepro.2021.128048

• Han, R., Yang, Y., Li, X., & Ouyang, D. (2018). Predicting oral disintegrating tablet formulations by neural network techniques. Asian Journal of Pharmaceutical Sciences, 13(4), 336–342. https://doi.org/10.1016/j.ajps.2018.01.003

• Henrique, G., & França, F. (2022). Advance time to determine injection and flushing times in drip fertigation. Horticulturae Article, 8(1103), 1–11.

• Irfan, M., Ramlie, F., Widianto, Lestandy, M., & Faruq, A. (2021). Prediction of residential building energy efficiency performance using deep neural network. IAENG International Journal of Computer Science, 48(3), 1–7.

• Jaafar, H., & Kharroubi, S. A. (2021). Views, practices and knowledge of farmers regarding smart irrigation apps: A national cross-sectional study in Lebanon. Agricultural Water Management, 248, Article 106759. https://doi.org/10.1016/j.agwat.2021.106759

• Kullu, P., Majeedullah, S., Pranay, P. V. S., & Yakub, B. (2020). Smart urban farming (entrepreneurship through Epics). Procedia Computer Science, 172(2019), 452–459. https://doi.org/10.1016/j.procs.2020.05.098

• Kumar, C. N., Selvam, S. P., Ramanathan, S. P., Kalarani, S., Nagarajan, G., & Duraisamy, S. (2022). Effect of drip irrigation and inter cropping systems on growth characters of maize. International Journal of Plant & Soil Science, 34(2), 36–42. https://doi.org/10.9734/ijpss/2022/v34i230834

• Liu, C., Wang, R., Wang, W., Hu, X., Wu, W., & Liu, F. (2022). Different irrigation pressure and filter on emitter clogging in drip phosphate fertigation systems. Water, 14(6), 1–18. https://doi.org/10.3390/w14060853

• Liu, J., Sun, B., Shen, H., Ding, P., Ning, D., Zhang, J., & Qiu, X. (2022). Crop water requirement and utilization efficiency-based planting structure optimization in the Southern Huang-Huai-Hai Plain. Agronomy, 12(9), 1–21. https://doi.org/10.3390/agronomy12092219

• Martinez, C. G., Wu, C. L. R., Fajardo, A. L., & Ella, V. B. (2022, June). Hydraulic performance evaluation of Low-cost gravity-fed drip irrigation systems under constant head conditions. In IOP Conference Series: Earth and Environmental Science (Vol. 1038, No. 1, p. 012005). IOP Publishing. https://doi.org/10.1088/1755-1315/1038/1/012005

• Mason, B., Rufí-Salís, M., Parada, F., Gabarrell, X., & Gruden, C. (2019). Intelligent urban irrigation systems: Saving water and maintaining crop yields. Agricultural Water Management, 226, Article 105812. https://doi.org/10.1016/j.agwat.2019.105812

• Mohamed, A. Z., Peters, R. T., Zhu, X., & Sarwar, A. (2019). Adjusting irrigation uniformity coefficients for unimportant variability on a small scale. Agricultural Water Management, 213, 1078–1083. https://doi.org/10.1016/j.agwat.2018.07.017

• Quimbita, W., Toapaxi, E., & Llanos, J. (2022). Smart irrigation system considering optimal energy management based on model predictive control (MPC). Applied Sciences, 12(9), 1–18. https://doi.org/10.3390/app12094235

• Rani, S. B., Venu, N., Ananthula, M. K., & Engli, A. (2022). IoT based smart irrigation system using node MCU. International Journal for Innovative Engineering and Management Research, 11(06), 100–106.

• Sezen, S. M., Ahmad, I., Habib-ur-Rahman, M., Amiri, E., Tekin, S., Oz, K. C., & Maambo, C. M. (2022). Growth and productivity assessments of peanut under different irrigation water management practices using CSM-CROPGRO-Peanut model in Eastern Mediterranean of Turkey. Environmental Science and Pollution Research, 29(18), 26936–26949. https://doi.org/10.1007/s11356-021-17722-w

• Suhardi, Marhaenanto, B., Taruna, B., Putra, W., & Winarso, S. (2023). IoT-based evapotranspiration estimation of peanut plant using deep neural network. INMATEH - Agricultural Engineering, 70(2), 487–496. https://doi.org/https://doi.org/10.35633/inmateh-70-47

• Wang, T., Melton, F. S., Pôças, I., Johnson, L. F., Thao, T., Post, K., & Cassel-Sharma, F. (2021). Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry. Agricultural Water Management, 244, Article 13. https://doi.org/10.1016/j.agwat.2020.106533

• Wang, Y., Li, S., Cui, Y., Qin, S., Guo, H., Yang, D., & Wang, C. (2021). Effect of drip irrigation on soil water balance and water use efficiency of maize in northwest China. Water, 13(2), Article 217. https://doi.org/10.3390/w13020217

• Zahid, B., Ansari, R., Cheema, M. J. M., & Anjum, L. (2020). Evaluation of deficit irrigation regime, row spacing and dual plantation of drip irrigated tomato under high tunnel. Journal of Central European Agriculture, 21(4), 851–860. https://doi.org/10.5513/JCEA01/21.4.2990

• Zhang, Y., Han, W., Niu, X., & Li, G. (2019). Maize crop coefficient estimated from UAV-measured multispectral vegetation indices. Sensors, 19(23), 1–17. https://doi.org/10.3390/s19235250

• Zhu, B., Zhang, Q., Yang, J. H., & Li, C. H. (2022). Response of potential evapotranspiration to warming and wetting in Northwest China. Atmosphere, 13(2), Article 21. https://doi.org/10.3390/atmos13020353