Construction of an intelligent agricultural irrigation control platform based on PLC technology

[1] Niswonger, R. G., Morway, E. D., Triana, E., et al. (2017). Managed aquifer recharge through off‐ season irrigation in agricultural regions. Water Resources Research,, 53(8), 6970-6992. Search in Google Scholar

[2] Ramanjaneyulu, A. V., Ramulu, V., Ramana, M. V., et al. (2022). Crop performance, water use, economics and energy indices in rabi groundnut (Arachis hypogaea L.) under micro irrigation methods. Journal of environmental biology, (2), 43. Search in Google Scholar

[3] Shi, L., Shi, G., Qiu, H. (2019). General review of intelligent agriculture development in China. China Agricultural Economic Review, 11. Search in Google Scholar

[4] Zhong, H., Sun, L., Fischer, G., et al. (2019). Optimizing regional cropping systems with a dynamic adaptation strategy for water sustainable agriculture in the Hebei Plain. Agricultural Systems, 173. Search in Google Scholar

[5] Li, R., Lin, H., Niu, H., et al. (2017). Effects of irrigation on the ecological services in an intensive agricultural region in China: A trade-off perspective. Journal of Cleaner Production, 156(jul.10), 41-49. Search in Google Scholar

[6] Veerachamy, R., Ramar, R., Balaji, S., et al. (2022). Autonomous Application Controls on Smart Irrigation[J]. Computers & Electrical Engineering, 100, 107855-. Search in Google Scholar

[7] Xing, Zikang., Ma, Miaomiao,. Wei, Yongqiang,. Zhang, Xuejun,.Yu, Zhongbo., Yi, Peng. (2020). A new agricultural drought index considering the irrigation water demand and water supply availability. Natural Hazards, 104(3). Search in Google Scholar

[8] Bhrugubanda, M., Rao, A., Shanmukhi, M., et al. (2021). Sustainable and Intelligent IoT Based Precision Agriculture-Smart Farming. Solid State Technology. Search in Google Scholar

[9] Valikhan-Anaraki, M., Mousavi, S. F., Farzin, S., et al. (2019). Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies. Sustainability, 11(8), 2337. Search in Google Scholar

[10] González, Perea, R., Camacho, Poyato, E., & Rodríguez, Díaz, J. A. (2021). Forecasting of applied irrigation depths at farm level for energy tariff periods using Coactive neuro-genetic fuzzy system. Agricultural Water Management, 256. Search in Google Scholar

[11] Zhang, F., He, C., Yaqiong, F., et al. (2022). Canal delivery and irrigation scheduling optimization based on crop water demand. Agricultural Water Management, 260. Search in Google Scholar

[12] Mason, B., M. Rufí-Salís, Parada, F., et al. (2019). Intelligent urban irrigation systems: Saving water and maintaining crop yields. Agricultural Water Management, 226. Search in Google Scholar

[13] Orojloo, M., Shahdany, S., Roozbahani, A. (2018). Developing an integrated risk management framework for agricultural water conveyance and distribution systems within fuzzy decision making approaches. Science of The Total Environment, 627(JUN.15), 1363-1376. Search in Google Scholar

[14] Benyezza, H., Bouhedda, M., Rebouh, S. (2021). Zoning irrigation smart system based on fuzzy control technology and IoT for water and energy saving. Journal of Cleaner Production, 302, 127001-. Search in Google Scholar

[15] Kumar, K. A., Jayaraman, K. (2020). Irrigation control system‐data gathering in WSN using IOT. International Journal of Communication Systems. Search in Google Scholar

[16] Boursianis, A. D., Papadopoulou, M. S., Gotsis, A., et al. (2020). Smart Irrigation System for Precision Agriculture - The AREThOU5A IoT Platform. IEEE Sensors Journal, PP(99), 1-1. Search in Google Scholar

[17] Wu, Di., Cui, Yuanlai., Li, Dacheng., Chen, Manyu., Ye, Xugang., Fan, Guofu., Gong, Lanqiang. (2021). Calculation framework for agricultural irrigation water consumption in multi-source irrigation systems. Agricultural Water Management, 244(1). Search in Google Scholar

[18] Zhang, Y., Wei, Z., Lin, Q., et al. (2018). MBD of grey prediction fuzzy-PID irrigation control technology. Desalination & Water Treatment, 110(APR.), 328-336. Search in Google Scholar