# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 3 Data Collection and Management Techniques
The cloud and AI application developed, Agroview, facilitates fast and accurate processing, analysis, and visualization of data collected from UAVs. In addition, it offers functionalities such as plant and gap detection, measurement of plant height and canopy size, and the creation of plant health maps. The application is applied to assess the phenotypic characteristics of citrus fruits, demonstrating its effectiveness with a low percentage Mean Absolute Percentage Error (MAPE) in detecting trees and estimating tree height and canopy size. The study focuses on the usefulness of cloud-based solutions, specifically using Amazon Web Services (AWS), to support the scalable and efficient deployment of Agroview, with different AWS instances allocated to specific processing tasks. The architecture includes a main application control machine, an assembly engine for CPU-intensive tasks, and a GPU-intensive instance for the tree detection algorithm. Data storage on AWS enables scalability and memory upgrades as required. Agroview offers a consistent, cost-effective, and fast solution for field surveys and plant phenotyping in specialty crop production. In response to the urgent need to improve agricultural practices in India, the study reported in [123] takes advantage of the Internet of Everything (IoE) to gather crucial data on the agricultural habitat, such as soil moisture, humidity, light, and temperature. The IoE system uses sensor nodes capable of transmitting monitoring data to the cloud. Then, data mining principles are applied to reveal meaningful patterns within the environmental data collected by the sensor network. The research, carried out as a use case on a farm in Chennai, introduces the peer-to-peer central registry-biased Internet of Everything Protocol (P2 PRioEP), serving as an application support registry for hybrid peer-to-peer IoT networks. The results obtained over a set period of time are presented and discussed. Data Quality Assurance  
Remote sensing is a major source of data, providing data for Earth observation and analysis from satellites, aircraft, and ground-based structures. In PA, a paradigm that emphasizes site-specific management, agricultural remote sensing is an essential technology. The authors of [124] explore the challenges and opportunities presented by agricultural remote sensing data, which share characteristics with general remote sensing data. The study provides an overview of remote sensing data resources, recent technological advances in remote sensing, big data management, and the processing and management of these data for PA. The paper proposes a bespoke Four-Layer, Twelve-Level remote sensing data management framework (FLTL), addressing the unique needs of PA. This FLTL framework, adapted from the Original Five-Layer, Fifteen-Level (FLFL) structure, serves as a comprehensive model for managing and applying agricultural remote sensing big data in PA and local agricultural studies. In the study reported in [125], the potential of combining time series of crop yield monitoring data from multiple fields and years into a unified dataset for predictive modeling using ML approaches was explored. Using data from large farms in Western Australia, the study integrated crop yield monitoring data for wheat, barley, and canola over three seasons (2013, 2014, and 2015), covering a vast number of hectares each year. The data were processed, gathering spatial and temporal predictor variables, and aggregated to a spatial resolution of 100 m for yield modeling. Random forest models were used to predict crop yields at different times of the season, corresponding to pre-sowing, mid-season, and end-of-season conditions. The models demonstrated accurate predictions, with root mean square errors ranging from 0.36 to 0.42 t ha\({}^{-1}\) and Lin's concordance correlation coefficients from 0.89 to 0.92 at field resolution. Forecast accuracy improved as the season progressed, in line with the increased availability of in-season information, such as precipitation.