# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 4 Applications and Case Studies
Experimental results indicate a 10.89% reduction in fertilizer use on average compared with traditional water-fertilization control systems, potentially saving 0.76 to 0.87 tons of fertilizer over the cotton growing season. The system involves monitoring nodes deployed on farmland, collecting soil and environmental parameters, processing the data via a microprocessor, and transmitting it to the central control unit. The decision support system checks data packets against predefined thresholds, such as soil EC values, and controls the pump start/stop signal according to the difference between soil EC detection values and fertilizer EC values, enabling more efficient application of water and fertilizer. In [154], an IoT-based fertilizer recommendation system for smart agriculture is presented, leveraging IoT devices and sensors to collect relevant agricultural data. The proposed framework uses ML techniques to provide fertilizer recommendations based on precise quantities and optimal timing. The data acquisition phase involves collecting input data such as soil temperature, humidity, weather data for specific regions, and crop details. Feature selection is performed using the sequential floating forward selection algorithm, and multilinear regression is used for data classification. Its performance is compared with Random Forest, C4.5, and Naive Bayes algorithms, with SFSS-MLR demonstrating superior accuracy, precision, recall, and F1 metrics, achieving an accuracy rate of 99.3%. In [155], an innovative IoT approach to improving crop productivity through real-time monitoring of soil nitrate levels is presented. Traditionally, fertilization decisions are guided by paper-based methods and canopy greenness measurements, leading to suboptimal applications and environmental issues. In response, the proposed system uses ion-selective membrane sensors and digital soil moisture probes to directly measure soil nitrate levels. Integrated with IoT connectivity, in particular Lora WAN transceivers, the system facilitates real-time data transmission to a cloud-connected gateway for processing and storage. The implementation of this soil sensor system aims to improve nitrogen use efficiency, support informed fertilizer management decisions, and mitigate nitrogen losses to the environment, thus contributing to improved agricultural sustainability. A study reported in [156] examines the potential of Diamond-Attenuated Total Internal Reflectance (D-ATR) Fourier Transform Infrared Spectroscopy (FTIR) as a soil nitrate (NO3\({}^{-}\)) sensor for the precise management of Nitrogen (N) fertilizers. The research uses two datasets: one from the field with 124 GPS-recorded soil samples collected from four agricultural fields, and another in the laboratory with five different soils enriched with varying amounts of KNO3. Using partial least squares regression, D-ATR-FTIR spectroscopy demonstrates promising results with R\({}^{2}\) calibration values of 0.83 and 0.90 for the field and laboratory datasets, respectively. In addition, robust cross-validation tests yield R\({}^{2}\) values of 0.65 and 0.83, demonstrating the potential of D-ATR-FTIR spectroscopy for rapid on-the-move determination of soil NO3\({}^{-}\) concentrations in the field. The field tests involve samples from four agricultural fields in central Iowa, taking into account variations in moisture, texture, organic matter, and other properties. The study suggests that this spectroscopic approach could enhance precise applications of nitrogen fertilizers, improving efficiency and reducing environmental impacts. Another study [157] aims to solve the problem of overfertilization in ornamental nursery production by investigating the effectiveness of two non-destructive sensors, Soil Plant Analysis Development (SPAD-502) and GreenSeeker\({}^{\text{TM}}\), in assessing nutrient uptake by plant tissues. The study uses Florikan Top-Dress 12N-6P-8K fertilizer on Justicia brandegeana with variable increments to simulate different nitrogen rates.