# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
Soil Sensors (moisture, temperature, nutrient levels)  
Soil sensors come in a variety of forms and measure essential soil properties such as moisture content, nutrient levels (nitrogen, potassium, and phosphorus), temperature, and PH. These sensors constantly monitor soil conditions to guide precise irrigation and fertilization practices, avoiding overuse of resources and ensuring optimum nutrient availability for crops [71]. The integration of embedded systems in agriculture, particularly in the context of smart greenhouse farming, is a vital and transformative development in the agricultural sector. This innovative solution aims to combat climate-related challenges and optimize crop yields. The smart greenhouse system, detailed in [72], offers localized automated agricultural control, enabling precise regulation of temperature, soil moisture, and light intensity to create an ideal environment for crop growth. Furthermore, the system allows remote greenhouse monitoring via the Internet, providing real-time data on crucial parameters. The system takes advantage of long-range connectivity, uses sensors for soil moisture, light, and temperature, and carries out automation by interfacing with actuators via a Nucleo Mbed STM board. Collected data is processed and uploaded to the cloud, facilitating remote monitoring for farmers. The integration of Microsoft Azure Cloud and Power BI ensures efficient data visualization and analysis, giving farmers the tools they need for informed decision-making and effective greenhouse management. This article presents the potential of integrated systems to revolutionize agriculture for improved productivity and sustainability. In [73], an IoT-enabled Wireless Sensor Network (WSN) framework was utilized, incorporating various soil sensors, environmental sensors, CO\({}_{2}\) sensors, and light intensity sensors to gather real-time agricultural data. These IoT-enabled WSN nodes collected and stored data, and a neural network-based prediction model was employed to optimize irrigation by managing water valves based on soil water requirement forecasts. The study conducted a comparative analysis of optimization techniques and found that the Variable Learning Rate Gradient Descent (VLRGD) outperformed Gradient Descent (GD) in soil moisture prediction. The research also generated a comprehensive soil moisture requirement map using interpolation and a Structural Similarity Index (SSIM)-based approach, leading to successful irrigation valve control commands based on fuzzy logic weather modeling to meet uniform irrigation needs under various weather conditions. The experiment was conducted in Bhubaneswar, India, on a farm with specific soil characteristics, and the results indicate the effectiveness of the proposed system for PA. Soil quality was assessed through laboratory analysis of a 3.5 kg soil sample, revealing dry bulk density and consistency values of about 1.43 g/cm\({}^{-3}\) and 0.46 g/cm\({}^{-3}\), respectively. The study deployed a WSN using multi-hop mesh network topology to interconnect all nodes. Each WSN node included an eight-channel Analog-to-Digital Converter (ADC) interfacing with various sensors, and communication occurred via an RS232 interface between the microcontroller (ATMEGA328P-PU) and Zigbee S2 units configured in a mesh network. The irrigation system employed sprinkler pipelines spaced at 0.41 m, with sprinkler heads delivering a flow rate of 2.2 L per hour (Lh\({}^{-1}\)). In [74], the authors assess the potential of portable Laser-Induced Breakdown Spectroscopy (LIBS) for determining total mass fractions of major nutrients (Ca, K, Mg, N, P) and trace elements (Mn, Fe) in soil, as well as other parameters such as humus content, soil pH, and plant-available P content. To address the dependence of LIBS nutrient quantification on the soil matrix, various multivariate regression methods, including PLSR, least absolute shrinkage and selection operator regression (Lasso), and Gaussian Process Regression (GPR), were used for calibration and prediction.