# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 4 Applications and Case Studies
The AREThOU5A platform aims to efficiently manage irrigated water in PA by integrating data from a wireless sensor network and satellite information. In particular, the platform uses LPWAN, RF energy harvesting, and ML to advance sustainable agricultural practices. The study includes the fabrication and validation of a rectenna module for RF energy harvesting, demonstrating promising results in an outdoor environment. The AREThOU5A IoT platform encompasses several subsystems, such as measurement, routing, user interface, and server subsystems, each contributing to its complete functionality. This holistic approach integrates various technologies to optimize water use strategies for growers, presenting a notable contribution to the field of PA.  
An irrigation support system based on automatic intelligent data mining for efficient irrigation management in agriculture is presented in [151]. The hybrid Convolutional Neural Support Vector Machine Classifier (CNSVMHC) is proposed as a key component for optimizing irrigation schedules using real-time soil Moisture Content (MC) data collected via a Wireless Sensor Network (WSN). The CNSVMHC, a hybrid of Convolutional Neural Network (CNN) and Support Vector Machines (SVM), facilitates a closed-loop control system, adjusting decision support schemes to account for approximation faults and local disturbances. The system aims to improve irrigation efficiency by taking into account soil, water, weather, and crop data, minimizing costs, maximizing crop productivity, and relying on real-time values rather than forecasts. The site-specific distributed irrigation system based on field sensors, combined with an intelligent Decision Support System (DSS), is highlighted for its potential to maximize harvest and quality while conserving water. The study incorporates robust estimation methodologies, including neural network algorithms, to rectify the farming system model, particularly in non-linear systems. The proposed approach uses a WSN environment for real-time monitoring and assessment of soil MC, collecting data from various sensors and storing it in a gateway node with Wi-Fi connectivity. The data collected in real time is then sent to a computer server for soil MC forecasting using CNSVMHC. The paper [152] discusses the urgent need for innovative approaches in agriculture to address challenges posed by rapid climate change, population growth, and shrinking variable land. It emphasizes the pivotal role of IoT technologies, particularly smart sensors, in revolutionizing greenhouse farming. Within an IoT-enabled greenhouse environment, numerous sensors and actuators are strategically deployed to continuously monitor and detect environmental changes, enabling data collection for analytics and supervision. The paper outlines a mesh network architecture for the greenhouse IoT system, featuring a local host with a GSM 2.5G receiver and microcontroller units. It also delves into the fundamentals of smart greenhouses, addressing emergency concerns like plant disease detection and exploring the efficiency of discriminant functions versus neural networks. The critical factors controlled within the greenhouse, including humidity, temperature, CO\({}_{2}\) levels, light, Electrical Conductivity (EC), pH, and dissolved oxygen, are monitored by corresponding sensors and adjusted by five types of actuators. These actuators aim to create the ideal atmospheric conditions, manage water conditions, and enhance lighting. The Decision Support System (DSS) serves as the central operating system, orchestrating all activities for optimal greenhouse management. Fertilizer Management  
The authors of [153] present a system for water and fertilizer monitoring based on soil conductivity thresholds, using a cost-effective wireless sensor network for data collection and transmission to a decision support system. Using soil Electrical Conductivity and changes in moisture content, the decision support system guides the application of water and fertilizer, improving the accuracy of the control system.