Agriculture is a vital sector in the global economy, supporting the livelihoods of billions of people and providing the food necessary to sustain the world’s population. However, the agriculture sector also faces critical challenges related to water scarcity1,2. The global water crisis, driven by over-extraction, climate change, and inefficient usage, has made water conservation a pressing priority. According to the United Nations, by 2025, two-thirds of the world’s population could face water stress3. This is particularly alarming given that agriculture accounts for approximately 70% of global freshwater consumption. The excessive use of water in irrigation, coupled with traditional farming practices that often lack efficiency and adaptability, exacerbates the problem, leading to water wastage and environmental degradation4,5.
Traditional irrigation systems, which rely on time-based or manual methods, have several disadvantages. These methods often do not take into account dynamic environmental factors such as soil moisture levels, rainfall patterns, and temperature variations, leading to inefficient water usage6,7,8. Furthermore, many farmers still use outdated technologies that are not capable of adjusting irrigation schedules in response to changing weather or soil conditions. As a result, crop yields may suffer from either under-irrigation or over-irrigation, which in turn affects agricultural productivity and resource sustainability2.
Climate-related smart agriculture, which combines IoT, sensor technologies, and data analytics, has emerged as a promising solution to optimize irrigation and improve crop management. These systems rely on climate data, soil moisture measurements, and weather forecasts to adjust irrigation schedules dynamically, reducing water wastage and enhancing productivity1,10. By integrating smart farming techniques with data-driven decision-making, climate-smart agriculture aims to mitigate the negative impacts of climate change on crop yields, making agriculture more sustainable and resilient11. To address these issues, various irrigation methods have been developed over the years. Conventional systems include surface irrigation, sprinkler systems, and drip irrigation, each with their own limitations. While drip irrigation is more water-efficient than traditional methods, it still relies heavily on manual monitoring and adjustments12,13. More recent advancements in irrigation technologies have focused on integrating sensors and automation to improve water management. The Internet of Things (IoT) has emerged as a transformative technology for addressing these challenges by enabling the automation and remote monitoring of agricultural systems1,3. IoT-based smart irrigation systems can collect real-time data from a variety of sensors, such as soil moisture and temperature, and use this data to optimize irrigation schedules14,15. Despite the promising potential of IoT, existing systems still face limitations in energy efficiency, reliability, and adaptability to different agricultural environments15,16.
This paper proposes an intelligent and automatic irrigation system that leverages IoT and fuzzy control technology9 to optimize water consumption. The system integrates a fuzzy rule-based inference engine to process sensor data and make irrigation decisions. Additionally, a fuzzy-based system is trained to determine the most appropriate irrigation method based on the environmental conditions. In addition, a fast fuzzy-based routing algorithm is designed in the existing system for information transmission. The system is designed to be low-cost, energy-efficient, and portable, allowing farmers to monitor and control irrigation remotely using mobile communication technologies like Global System for Mobile (GSM).
The proposed intelligent irrigation system introduces a novel integration of fuzzy logic-based irrigation control, deep neural networks (DNNs) for decision-making, and an energy-efficient Open Shortest Path First (OSPF)-based routing mechanism to optimize water usage and enhance network lifetime. Unlike conventional systems that rely solely on predefined threshold-based decision-making, our approach leverages historical and real-time data to dynamically determine the best irrigation schedule. The Fuzzy Inference System (FIS) ensures adaptive and precise irrigation control, while the DNN model refines predictions based on environmental conditions. Additionally, the enhanced OSPF-based routing algorithm improves network energy efficiency by selecting optimal communication paths. These innovations collectively enhance the scalability, adaptability, and efficiency of IoT-driven irrigation systems, setting our method apart from existing solutions.
The main contributions of this work are as follows:
The development of an intelligent irrigation system based on fuzzy control, enabling real-time decision-making for optimized water use.
Integration of DNNs with a fuzzy-based system to enhance adaptability and accuracy in selecting the optimal irrigation method based on dynamic environmental conditions.
A fast fuzzy-based routing algorithm designed to improve energy efficiency and extend the network lifetime in WSNs.
A low-cost, energy-efficient, and portable system suitable for various agricultural applications, including farms and greenhouses.
The use of IoT-driven smart farming techniques to enable remote monitoring and management, improving irrigation precision and resource utilization.
The remainder of this paper is organized as follows: Section 2 reviews related work in the field of IoT-based irrigation systems. Section 3 summarizes some concepts and definitions relevant to this study. Section 4 describes the system architecture and design. Section 5 presents the simulation setup and results, while Sect. 6 concludes the paper and discusses future work.