# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 4 Applications and Case Studies
Parameters such as NDVI, SPAD readings, total leaf Carbon:Nitrogen (C:N), and soil leachate analysis are recorded over three months. The results indicate that a smaller quantity (20 g) of Controlled-Release Fertilizer (CRF) is as effective as larger quantities (30, 40, and 50 g) in supplying nitrogen to J. brandegeana. The use of SPAD and GreenSeeker\({}^{\text{TM}}\) optical sensors demonstrates promising results for determining fertilizer requirements in ornamental plant production, offering a rapid, non-destructive approach to sustainable fertilizer management practices in the industry. Nutrient and fertilizer sensors monitor nutrient concentrations in real time, enabling precise and efficient nutrient distribution [158]. They ensure that crops receive the right nutrients precisely at the right time, reducing waste and minimizing environmental impact [159]. All of these types of smart sensors play a crucial role in improving farming techniques, reducing resource use, and enhancing crop production [160]. In this context, the integration of these sensors into an interconnected system forms the foundation of PA, enabling an era in which data-driven information directs all aspects of farming, from sowing to harvesting. Pest and Disease Monitoring  
Plant diseases, induced by elements such as pests, pathogens, and climatic changes, represent substantial threats to ecosystems and human life. Innovative sensor technologies for real-time monitoring and prediction of plant health are imperative to counter potential crop losses resulting from various stresses. Wearable plant sensors, positioned directly on organs from leaves to stems, seek to assess plant health by analyzing biomarkers and microenvironmental variables. These sensors, classified as plant growth, physiology and microclimate sensors, multifunctional sensors, and chemical sensors, offer promising applications in PA [161]. A revolutionary approach to real-time plant monitoring is proposed by a study [162] based on the development of a bionic plant sensor. This wearable plant sensor, featuring a nanometer-thick Ag film and double V-shaped grooves, addresses the limitations of existing multifunctional sensors, such as high manufacturing costs and suboptimal performance. The bionic sensor demonstrates exceptional vibration sensitivity and is manufactured using a simple, scalable strategy, with a remarkably low production time of one hour and a cost of USD 1.3 per unit. In addition, the wireless functionality enables continuous, real-time monitoring of bamboo growth, revealing day and night growth trends in line with established intrusive reports. The results suggest that this wearable, cost-effective plant sensor shows considerable promise for advancing real-time plant growth detection in PA.  
Another study [163] explores advanced agricultural techniques for optimizing crop growth and yield by leveraging IoT technology for comprehensive crop monitoring and data collection. Research highlights the importance of understanding environmental conditions, soil quality, water, and fertilizer requirements throughout plant growth phases to improve agricultural productivity. Using an IoT-enabled monitoring system, the study aims to detect crop diseases and assess the reproductive status of pathogenic pests. Data analysis carried out on samples collected throughout the entire crop growth cycle reveals valuable information on the correlation between crop yield, weather parameters, and insect reproduction. Using a fuzzy inference system, the knowledge base is built using these meteorological parameters, and a multi-objective evolutionary algorithm applies fuzzy rules to identify suitable crop windows with low pest reproduction conditions and maximum crop yield. The proposed approach, tested on rice and sugarcane crops in the agricultural fields of Gwalior, India, uses an IoT-enabled wireless sensor network (IEEE 802.15.4) to collect data on soil moisture, precipitation, temperature, etc.