# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
Greenhouses provide an artificial environment for plants, effectively protecting them from weather, pests, and disease, reducing risk, and optimizing crop yields. The authors in [66] implement a wireless sensor network to monitor parameters such as temperature, humidity, and soil moisture, ensuring automatic control of climatic conditions. The data from each sensor node is transmitted to the cloud, where it can be monitored and analyzed. Actuators further enhance the system by adjusting growth parameters when anomalies are detected. This system provides 24/7 monitoring and control for consistent, optimized crop production, while users can access growth data in real time via a responsive web page set up on a local ESP32-based server, offering temperature and irrigation control based on predefined threshold values. The system architecture comprises a Wireless Sensor Network (WSN), a Network Coordinator (NC), and a Web Server (WS), ensuring modularity and scalability for efficient data collection and management. The integration of sensors such as the DHT11 temperature and humidity sensor, as well as resistive soil moisture sensors, enables accurate data acquisition for informed decision-making, improving greenhouse farming practices. An automated smart greenhouse system [67], leveraging an Adaptive Neuro Fuzzy Inference System (ANFIS) and the IoT, presents an innovative solution for enhancing crop production. This system collects real-time data from sensors measuring four critical weather parameters: temperature, humidity, sunlight, and soil moisture. The gathered data serves as input for a fuzzy control system, which manipulates the information, and ANFIS pre  
\begin{table}
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**Parameters** & **Standard** & **Frequency** & **Data Rate** & \begin{tabular}{c} **Transmission** \\ **Range** \\ \end{tabular}  & \begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular}  & **Cost** & **Limitations** \\ \hline Wi-Fi & \begin{tabular}{c} IEEE 802.11 \\ [59] \\ \end{tabular}  & \begin{tabular}{c} 2.4 GHz and \\ 5 GHz bands \\ \end{tabular}  & \begin{tabular}{c} 11–50 and \\ 150 Mbps \\ \end{tabular}  & \ \begin{tabular}{c} 20–100 m \\ 150 Mbps \\ \end{tabular}  &
\begin{tabular}{c} High \\ 150 Mbpsdicts optimal weather parameter values. Farmers can monitor and adjust temperature and humidity through a mobile app. Data transmission occurs via GSM or TCP/IP, with IoT perception layer data being transferred to the application layer. Security measures are in place, including the detection of potential IoT perception layer attacks and their mitigation. The working procedures of various sensors in this automated greenhouse system are intricately designed to ensure precise environmental control. The DHT11 temperature and humidity sensor was used to measure ambient temperature and humidity every two hours during the day, and at four-hour intervals at night. These sensors connect to a high-performance 8-bit microcontroller, which then transmits the data to the FIS. If temperatures fall below 20 \({}^{\circ}\)C or rise above 30 \({}^{\circ}\)C, the microcontroller triggers a heater or cooling fan, maintaining the desired range. The humidity sensor, also using the DHT11 sensor, tracks humidity levels and activates exhaust fans if humidity exceeds 70% or fans if it falls below 65% RH. Soil moisture is managed by the YL-69 soil moisture sensor, prompting water pump activation when soil moisture drops below 70%. The Light-Dependent Resistor (LDR) sensor maintains optimal lighting conditions, switching lights on when insufficient light is detected. The system performs effectively, detecting attacks with a 93.62% accuracy rate, making it superior to existing systems. It is suitable for both large-scale and small-scale agriculture, offering precise soil moisture measurement and greenhouse maintenance. Customizable and efficient, the system combines IoT and fuzzy logic, enhancing learning efficiency and prediction accuracy.