Real-time Monitoring of Rural Water Systems for Irrigation Farming and its Economic Implication – A Case Study of Nasarawa, Nigeria
DOI:
https://doi.org/10.54536/ajise.v4i1.2828Keywords:
Irrigation, Wireless Sensor Networks, Agriculture, Machine Learning, ModellingAbstract
Water security plays a significant role in many developing countries’ socio-economic growth. On the other hand, irrigation water farming is important in improving people’s livelihood in areas with limited water resources. However, in some communities in sub-Saharan Africa, most farmers depend on gravity-powered water systems to water their crops. This natural irrigation method is inefficient for commercial farming as it can lead to the overuse of scarce water resources. Moreover, these systems easily fail due to inadequate monitoring regimes. Therefore, to automate and improve irrigation water efficiency for agricultural purposes, a technology-designed artificial intelligence-based monitoring model must be developed for agricultural irrigation water systems in rural communities. This research investigated the economic effects of real-time monitoring of rural water systems for irrigated farming in Nasarawa, Nigeria. The study also examined the effectiveness and productivity of irrigation farming using a case study methodology. Also, the research applied machine learning techniques and wireless sensor networks to model a sustainable irrigation water system for soil moisture and rainfall detection. The model is embedded with artificial instructions that prompt it to take a given action based on the sensor message. The model was tested on a farm settlement in Nasarawa state, Nigeria. The results of this study have significantly contributed to the body of knowledge on real-time monitoring of rural water systems for agricultural irrigation. The study’s findings also shed light on the possible financial advantages of real-time monitoring for farmers and the neighborhood economy.
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