# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 4 Challenges and Future Directions  
As a result of our investigation into smart irrigation systems, we found that none of the proposed solutions meet all of our specified problem space requirements. For instance, relying solely on soil moisture sensors will bring in unreliable inputs, which will cause biased or wrong results. In contrast, integrating many sensors into one system will increase cost, energy consumption, hardware, and maintenance costs. Thus, practitioners need to balance the three dimensions to make a choice. The following summarizes the discussions about the current challenges and potential research directions. * _Hardware and Software Failure_: One of the key challenges in IoT irrigation systems is hardware and software failure. For example, while most sensors are small and affordable, they are also prone to damage, especially in harsh operational environments. This can lead to inaccurate data, posing a significant challenge. To address this, it is crucial to design novel technical solutions to detect hardware and software failures, automatically troubleshoot the root cause of failures, and implement a cost-effective failure-tolerant plan for recovery, ensuring data accuracy and system reliability. * _High Cost_: The installation of an IoT irrigation system needs investment, such as power supply, sensors, base stations, and application development. Thus, the overall cost for a complete irrigation system is not cheap, especially for a system that integrates various types of sensors for individual plants. As one solution, LPWAN technologies can solve a range of concerns and will be a future trend in IoT irrigation systems. However, the high installation costs of LPWAN base stations, plus the complex field conditions affecting the communication performance, need further study to reduce costs and improve the performance. * _User Training_: After the installation, farmers need to be trained on how to use the application, how to control the irrigation system, how to identify potential risks, how to detect failures, and how to charge the battery, which needs continuous training with the farmers to ensure data reliability. Farmers in developing countries lack access to network or computer systems. Thus, how to ensure that farmers receive ongoing training to adapt to these rapid changes in the IoT world is another challenge we are facing. To this end, related curriculum and training materials need to be developed for farms with real-world practice and assist farms in accessing state-of-the-art technology. * _Data Transmission_: LPWAN is suitable for its long coverage range. However, the bandwidth is so narrow that only text messages can be transmitted. When using drones to collect data from sensors to solve the data size problem, sensors should add a data storage feature function to save the data until the drone flies by [31; 52]. Microcontrollers need to be added on top of sensors to meet the requirement, increasing the unit cost for the sensor system. New network technology needs to be developed to provide more bandwidth with long-distance coverage. Additionally, the state-of-the-art AI technology can be leveraged to the smart irrigation systems to tune network settings to optimize the required network performance. * _Water Utilization_: Even with efficient data collection and analysis, there is a risk that new pollution events may occur between monitoring intervals, leading to the contamination of irrigation water. Developing sensors that can rapidly and accurately detect various types of water pollution is an ongoing challenge. New sensor technologies and techniques may be needed to address emerging contaminants. Integrating additional sensors and technologies to enhance water quality monitoring can increase the overall cost of the irrigation system, potentially impacting its affordability for users. It is essential to balance the need for rapid detection and the solution's cost-effectiveness all while ensuring the long-term sustainability of agriculture practices.