PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• 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