# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 3 Data Collection and Management Techniques
Recognizing the predominance of traditional vineyards and obsolete drying structures, the research highlights the need for modernization to improve quality and productivity without imposing excessive costs on traditional agriculture. To achieve this, the study proposes the implementation of a Wireless Sensor Network (WSN) system for real-time remote monitoring of micrometeorological parameters in three geographically dispersed vineyards. The WSN system facilitates data collection, enabling farmers to make informed decisions about irrigation scheduling, disease prevention, and optimal harvest times. The system integrates agrometeorological sensors in vineyards and drying structures with a gateway for data collection and wireless transmission to a central server. In addition, an online warning system alerts farmers in real time by SMS. Field experience validates the system's functionality, demonstrating its effectiveness in the development of information systems for precision viticulture. The authors of [116] explore the integration of computing technologies, including the IoT, WSNs, data analytics, and machine learning, in the context of agriculture, focusing on the prediction of apple diseases in orchards in the Kashmir Valley. The study proposes a predictive model using data analytics and ML within an IoT system. In addition, a local survey assesses farmers' views on emerging technologies for PA. The practical implementation involves setting up a WSN with sensors and a gateway to capture data in apple orchards. Six sensors, each comprising an IRIS with an MTS 420 sensor board, cover specific areas of the orchard. The gateway, using MIB 520 with an IRIS, facilitates connectivity and communication. The document details the programming steps using the MoteConfig application for IRIS and MIB 520, ensuring correct configuration for efficient data collection and analysis. Wireless Sensor Networks  
The evolution of modern wireless communications and information technologies provides the foundation for PA. The authors of [117] systematically explore viable wireless communication technologies for PA through an analysis of agricultural application scenarios and experimental tests. The study presents the potential of three Wireless Sensor Network (WSN) architectures based on NB-IoT (narrowband), LoRa, and ZigBee wireless communication technologies for precision farming applications. Their feasibility is rigorously tested, and comparative evaluations are carried out by measuring the normal communication time and power consumption levels achieved by these wireless communication technologies. A comprehensive field test and analysis reveals that ZigBee is the optimal choice for agricultural plant monitoring, while LoRa and NB-IoT emerge as two wireless communication technologies suitable for agricultural field scenarios. The research provides valuable information on the selection of wireless technologies suited to the specific needs of PA. Artificial Intelligence has made significant progress in various monitoring and control applications, particularly in the agricultural field. However, the development of low-power sensing devices incorporating AI remains fragmented in current research. The authors of [118] present an embedded system that integrates AI for continuous analysis and on-site prediction of plant leaf growth dynamics. The system uses an integrated low-power sensing system combined with a Graphics Processing Unit (GPU) capable of running neural network-based AI applications on board. A Recurrent Neural Network (RNN), in particular the Long-Term Memory Network (LSTM), is the main component of this system. In addition, the proposed approach ensures autonomous operation for up to 180 days on a standard Li-ion battery, leveraging state-of-the-art mobile graphics chips for intelligent analysis and monitoring of autonomous devices.