# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### GPS and Remote Sensing Technologies  
The use of GPS and remote sensing technologies such as satellites and drones provides access to detailed, high-resolution spatial information. These advanced tools facilitate accurate mapping, crop yield monitoring, and the creation of precisely defined field boundaries [83]. They represent invaluable tools for assessing crop performance and improving the efficient allocation of resources in agriculture [84]. In this context, numerous studies have reviewed remote sensing techniques and their applications in PA. Several studies have concentrated on specific application areas such as soil and crop management, Evapotranspiration (ET) management, and disease and pest management, while others have covered more than one application area [85; 86; 87; 88]. Table 3 provides examples of satellites used for PA and their applications. \begin{table}
\begin{tabular}{l l} \hline \hline
**Satellite Name** & **Application in PA** \\ \hline ECOSTRESS & Evapotranspiration (ET) [89] \\ TERRA-ASTER & Water management [90] \\ Sentinel-1A & Soil Moisture [91] \\ Sentinel-2 & Crop management [92] \\ Terra/Aqua MODIS & Crop yield [91] \\ KOMPSAT-2 & Crop yield [93] \\ RapidEye & Crop yield [94] Soil water [95] \\ GeoEye-1 & Nutrient management [96] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of Satellites Used for Precision Agriculture and their Applications. In the PA field, drones have gained importance, being used for remote sensing tasks such as photo and video capture, as well as functions such as fertilization or animal deterrence. The authors of [97] assess the viability of using remote-sensing drones as mobile gateways, taking advantage of their pre-set flight plans. In contrast to existing proposals, this study innovatively assesses the performance of UAVs as low-cost remote sensing tools and gateway nodes, collecting data from field-deployed nodes in PA systems for crop monitoring. Simulations in various scenarios, taking into account factors such as drone speed, flight height, node density, and antenna coverage, were carried out to verify the feasibility of accurate data transmission. The results indicate specific combinations, such as lower speed, 24 m flight height, and 25 m antenna coverage, for optimal connectivity under maximum node density, while various combinations offer good and optimal connectivity for other node densities. This research provides valuable information for selecting optimal drone parameters for efficient Wi-Fi data transmission in PA systems. In [98], the challenges of deploying PA infrastructure, in particular Decision Support Systems (DSS), in small-farm contexts due to costs and technical limitations are discussed. It exploits freely available satellite data (Sentinel-2A) and the SNAP toolbox in the context of Open-Source Remote Sensing (OSRS) to demonstrate the potential for monitoring crop health and development. Using the Tono irrigation system in Ghana as a case study, this research demonstrates the effectiveness of OSRS in providing valuable information for agricultural management and resource allocation decision-making. SNAP Toolbox's vegetation index algorithms are proving capable of accurately identifying crucial crop conditions such as chlorophyll content, nitrogen status, pest and disease infestations, and water requirements. However, for this innovative OSRS-based monitoring system to be cost-effective, the article highlights the need for basic training for scheme managers and extension workers in interpreting the results of OSRS analysis. Implementing OSRS as a DSS for smallholder farmers in resource-poor contexts has significant potential to reduce costs, optimize resource allocation, and improve yields. The authors of [99] investigate the estimation of durum wheat yield using various technologies and data processing methods. The use of an advanced data cleaning technique on yield monitoring system data results in close agreement between yield monitoring and manually sampled data.