# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 3 Data Collection and Management Techniques  
To create effective monitoring solutions, there are a number of potential benefits to be gained from integrating agronomic knowledge with emerging technologies such as the IoT, cloud computing, and smart systems. There are some commonalities between various studies, including the use of low-cost devices, low-power sensor nodes (Arduino(r) and ESP8266 microcontrollers), and communication protocols such as ZigBee and IEEE 802.11. Gateways play a crucial role in data aggregation and local processing within the Fog computing concept, with some studies transmitting data directly to remote web/cloud services. In remote locations, GSM/GPRS is a widespread solution. On the server side, data analysis services are increasingly accessible to end-users via mobile or PC applications. The high processing capacity of the cloud enables efficient data handling, particularly when processing large volumes from numerous low-cost devices. In particular, there is a growing trend towards proximity surveillance scenarios involving the use of images, introducing increased processing requirements for capture elements and transmission via remote servers [113]. Collecting data in the field is essential for efficient precision farming, ensuring the optimal use of resources such as water, fertilizers, and pesticides. The integration of GPS receivers on farm equipment facilitates precise positioning in global coordinates, a crucial aspect for site-specific management and instrument navigation. The use of multiple positioning sensors, including GPS and inertial sensors, improves the accuracy of vehicle navigation in the agricultural sector. The adoption of intelligent farming techniques involves automated data collection via computer-aided systems, in which wireless technology plays a crucial role in eliminating cables, thus reducing maintenance problems and costs. Data management tools hosted on cloud servers enable fast and accurate operations, with analysis tools and pre-configured algorithms improving overall efficiency. The authorsof [53] introduce the concept of TV Whitespace (TVWS), unused bandwidth between active TV channels, as a solution for wireless data transmission in agriculture. TVWS, often overlooked, offers the possibility of efficient data transmission, with applications in remote sensor data collection, farm management, and drone imagery. The use of TVWS in the development of 5G transmission further underlines its potential to revolutionize PA by offering faster transmission speeds and extended range compared with traditional communication channels. Data Acquisition Systems  
To examine the challenges and potential of UAV-based multispectral remote sensing in PA, researchers in [114] used a narrowband Mini-MCA6 camera and a broadband Sequoia camera mounted on a micro-UAV for data collection in corn fields. The research highlights the importance of appropriate calibration methods, in particular the non-linear sub-band empirical line method, for accurate reflectance values, with the Mini-MCA6 camera proving more accurate in the visible bands. However, the accuracy of the Vegetation Indices (VI) does not depend entirely on reflectance accuracy, highlighting the variations between cameras. Mini-MCA6's NDVI outperforms Sequoia's, while Sequoia excels in red-edge NDVI (reNDVI). At the plot level, reNDVI outperformed NDVI in predicting Soil Plant Analysis Development (SPAD) values under different nitrogen treatments. The study highlights the instructive role of multispectral UAV remote sensing in PA but calls for further progress in calibration methods, post-processing techniques, and robust quantitative surveys. The authors of [115] address the importance of grape quality and the drying process in raisin production, focusing particularly on southern Azerbaijan, a crucial region for grape cultivation.