Fuzzy-Based Irrigation Improvement System for Nigerian Agricultural Fields

Abstract

Efficient water management in agriculture is crucial for ensuring sustainable crop production and addressing the global water crisis. Traditional irrigation systems often suffer from inefficiencies, leading to water wastage, uneven crop yields, and increased costs. This paper proposes a fuzzy-based irrigation improvement system that leverages advanced machine learning algorithms, Internet of Things (IoT) devices, and real-time environmental data to optimize water usage in agricultural fields. The system integrates sensors to monitor key parameters such as soil moisture, temperature, humidity, and weather forecasts. Fuzzy logic algorithms analyze this data to determine precise irrigation schedules and water requirements, minimizing wastage and ensuring optimal hydration for crops. The system also employs predictive analytics to adapt to changing environmental conditions and crop growth stages. Key benefits include significant water savings, increased crop yield, reduced operational costs, and enhanced sustainability. Additionally, the system can be scaled and customized to suit different types of crops, soil conditions, and geographical locations. This approach demonstrates the potential of fuzzy logic in transforming traditional farming practices into a data-driven, resource-efficient paradigm. Future developments will focus on integrating renewable energy sources and blockchain for enhanced transparency and efficiency. This fuzzy-driven solution promises a sustainable future for agriculture by addressing the dual challenges of water scarcity and food security.