# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### Water Quality Sensors  
Water quality sensors are responsible for monitoring various aspects of irrigation water, including salinity, dissolved oxygen levels, and turbidity. These sensors play an essential role in preserving the suitability of irrigation water for cultivation and protecting against soil deterioration caused by excess salts or contaminants [102]. The application of sensors in PA is a pivotal component of automated irrigation systems, addressing the challenges of overwatering and underwatering crops, thereby optimizing resource utilization. This system [103] leverages wireless technology and a Wireless Sensor Network (WSN) with IoT principles, ensuring efficient communication between sensor nodes and the central control unit. Temperature, humidity, soil moisture, and water tank levels are continuously monitored, and the data is transmitted to users via GSM communication. This smart irrigation system caters to the specific water requirements of different crops in various areas, facilitating precise and cost-effective agriculture management, particularly for Indian farmers. The WSN, coupled with IoT, enables remote data control and analysis through a user-friendly Android application, offering both automated and manual operation options. The system architecture, primarily centered around the nodeMCU, connects sensors and hardware components while integrating GSM and Wi-Fi modules for seamless communication and data storage, ultimately enhancing the efficiency of PA practices. Efficient water use in agriculture is a crucial challenge addressed by modern technologies, in particular through Irrigation Advisory Services (IAS) and Decision DSS. The authors of [104] present the results of the project \"An Advanced Low-cost System for agricultural Irrigation Support--LCIS\". The LCIS-DSS, developed as part of this project, integrates three methodologies: W-Tens (in situ soil sensor), IRRISAT(r) (remote sensing), and W-Mod (Water balance simulation Modeling). The evaluation carried out on maize crops in southern Italy during the 2018 season assesses the predictive performance of the tools as well as their advantages and disadvantages, taking into account applicability on a spatial scale as well as costs and complexity of use. While all three approaches contribute to maximizing corn production, the in-situ soil sensor method (W-Tens) delivers 40% more water, underscoring its efficiency, while IRRISAT(r) and W-Mod excel in Irrigation Water Use Efficiency (IWUE). IRRISAT(r) stands out in particular for its ability to operate without spatial soil information. The study highlights the potential of integrated tools to improve agricultural water use efficiency, recognizing the role of field sensors and remote sensing in this context. The authors of [105] propose a scalable Wireless Sensor Network (WSN) architecture integrated with the IoT to Enhance Precision Agriculture and Farming (PAF) practices, particularly focusing on efficient irrigation water resource management. The system relies on IoT for communication and data processing in remote agricultural areas. Throughput maximization, latency minimization, high Signal-To-Noise Ratio (SNR), minimum mean square error, and improved coverage area are evaluated to optimize the WSN structure. Results demonstrate the superiority of this approach over conventional IoT-based PAF systems. Furthermore, the paper introduces a Smart Irrigation System for PA and Farming (SIS-PAF) utilizing IoT and cloud computing to address water scarcity issues by reducing irrigation wastage. The system incorporates hardware components like Arduino Uno, moisture sensors, and a GSM modern for wireless communication. Data collected from Arduino is transmitted via GSM to a webpage, allowing farmers in remote areas to monitor and control irrigation, adjusting it based on soil moisture levels. Machine Learning (ML) is employed in a two-phase process to optimize water supply, and ultrasonic sensors track plant growth.