# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 4 Applications and Case Studies  
The literature provides an in-depth exploration of the role of WSNs in agriculture, focusing on numerous crops to assess WSN applicability. The main objective is to use WSNs for crop monitoring, irrigation management, and the measurement of various environmental parameters crucial for better crop growth and quality. The researchers studied the impact of WSNs on eighteen different crops, ranging from broccoli, cotton, oranges, and orchids to sugarcane, grapes, peppers, potatoes, citrus, blueberries, corn, wheat, peaches, and more [35]. Importantly, WSNs are widely used to monitor crop growth in greenhouses. Another area of the research looks at the communication technologies adopted in WSNs for PA, with Bluetooth, ZigBee, Wi-Fi, and GPRS/GSM identified as the predominant options. Notably, WiMAX technology is currently excluded from PA applications for reasons of cost and size. ZigBee appears to be the most popular communications technology due to its wide transmission range and cost-effectiveness. GSM/GPRS comes in a close second, making it the second most widely used communication technology in PA via WSNs [35]. WSNs consist of interconnected wireless nodes equipped with radio transceivers, microcontrollers, sensors, and antennas to monitor environmental parameters. Sensors, measuring various factors such as soil statistics, crop characteristics, weather conditions, and resource requirements, transmit data to controllers, which then relay the information to the cloud or portable devices [126]. The agricultural sector, with its multifaceted demands, benefits from WSN technologies, facilitating real-time monitoring of crop behavior. Continued advances in WSN technologies have led to a reduction in the size and cost of sensors, enabling their widespread implementation in various sectors, including agriculture. Table 4 presents examples of sensors used in PA. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{1}{c}{**Sensor Name**} & \multicolumn{1}{c}{**Parameters**} & **Reference** \\ \hline SDI-12 Sensor & Soil Moisture & [127] \\ \hline Hydra probe II soil sensor & Soil Moisture, Soil Temperature, Conductivity and Salinity level & [128] \\ \hline ECH2O-5 TE Sensor & Soil Moisture content levels, Soil Electric Conductivity, Soil Temperature, Soil Organic Content, Soil Texture, and Soil Bulk Density & [129] \\ \hline ECH2O EC-5 Sensor & Soil Moisture, Soil Temperature, Soil Water & [130] \\ \hline EC250 sensor & Soil Temperature, Soil Moisture, Salinity level, and Conductivity & [131] \\ \hline SKU: SEN0193 sensor & Soil Moisture, Soil Temperature & [132] \\ \hline VH-400 sensor & Soil Moisture sensor, Soil Temperature & [133] \\ \hline DHT11/DHT22 sensor & Temperature and Humidity sensor & [134,135] \\ \hline SHT31 Sensor & Temperature and Humidity sensor & [136] \\ \hline SHT71, SHT75 & Temperature and Humidity sensor & [137] \\ \hline TAOS TSL262R & Luminosity sensor & [138] \\ \hline TGS4161 & CO2 sensor & [139] \\ \hline S-THB-M002 sensor & Temperature and Humidity sensor & [140] \\ \hline TCS3472 RGB sensor & Light sensor & [141] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Examples of Sensors Used in the Precision Agriculture Domain. Precision Irrigation  
The study reported in [147] focuses on solving the challenges of irrigation efficiency, water scarcity, and labor-intensive tasks in agriculture. It proposes an intelligent irrigation system aimed at contributing to PA. Integrating various sensors to measure water reservoir levels, humidity, temperature, soil moisture, and precipitation, as well as retrieving weather forecasts for temperature and precipitation, the system uses an algorithm programmed using Python 2.7 involving a relay control approach, given the complex decision-making process described. Using this algorithm, the smart irrigation system optimizes resource use, maximizing crop yields.