# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### Water Quality Sensors
Overall, this automated system empowers farmers with real-time data on soil conditions, crop growth, and environmental parameters, ultimately improving crop productivity and water resource utilization in PA.  
A proposed study in [106] presents a low-power wireless sensor network using the LoRaWAN protocol designed for cost-effective PA applications, with a focus on greenhouse detection and actuation. The research focuses on a modular system built from readily available components and open-source software libraries. The experiment validates the system's reliability through continuous testing with two natural soils, Silty Loam and Loamy Sand. In particular, the study compares the performance of a low-cost Volumetric Water Content (VWC) sensor with a reference sensor from Sentek, revealing a non-constant sensitivity for the former. The proposed system uses a new procedure to optimize parameters and extract reliable VWC values, introducing a unique approach to address the sensor's lack of linearity. The layered architecture integrates wireless nodes, a LoRaWAN network, and cloud-based applications, offering real-time data visualization via a graphical user interface. While acknowledging the need for further optimization, the study presents a promising method for improving the performance of low-cost sensors in PA applications. The authors in [107] present a new approach, the Deep Learning-based IoT intelligent irrigation system for precision agriculture (DLiSA). Using a Long Short-Term Memory Network (LSTM), DLiSA predicts volumetric soil moisture content, irrigation timing, and spatial water distribution for arable land one day in advance. The integrated feedback system is designed to maintain functionality in a variety of weather conditions and regions. Simulation results demonstrate that DLiSA outperforms state-of-the-art models in terms of judicious water use in experimental agricultural areas. The focus is on crucial agricultural aspects such as water conservation and control of irrigation periods. Calibration and testing throughout one and a half years at three sites demonstrate DLiSA's reliability, and its performance compares favorably with existing models for predicting soil moisture content over one month. The authors highlight the significant water savings achieved by the proposed model compared with alternative models, keeping the soil moisture deficit within acceptable limits. Future research aims to improve the predictive capabilities of the irrigation scheduler by integrating the prediction of rainfall depth, further optimizing water conservation through efficient use of rainfall. * Pest and Disease Detection Sensors  
Some sophisticated sensors have the ability to identify the existence of pests and diseases in crops. They use methods such as image recognition, spectroscopy, or other techniques to discern the presence of pathogens or pests, enabling rapid action to mitigate potential damage [108; 109]. The study conducted in [110] delves deeper into the field of intelligent agriculture by introducing an embedded system enriched with ML functionalities for the continuous detection of pest infestations in fruit orchards. Although artificial intelligence has made significant inroads in various sectors, including agriculture, the focus on low-power detection devices with embedded ML capabilities remains rare in smart agricultural applications. The paper focuses on the critical problem of biotic stress affecting crop yields and addresses it through the development of a smart trap using ML algorithms for rapid pest detection in apple orchards. The integrated solution incorporates a Neural Accelerator, enabling image capture and processing in pheromone-based traps. The system has trained and deployed three ML algorithms, demonstrating the platform's versatility. The proposed approach ensures extended battery life thanks to energy recovery components, enabling continuous, autonomous detection of pest infestations without the need for frequent farmer intervention.