Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Abstract

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

“…The UAV corresponds to a pilotless aircraft, i.e., a flying machine operated with no humans (passenger/pilot) onboard [ 78 ]. Some previous literature reviews and meta-analysis explored the use of UAVs in agriculture, as in research by Lelong et al (2012) [ 79 ], Singh and Frazier (2018) [ 80 ], Shahbazi et al (2014) [ 81 ] and Eskandari et al (2020) [ 82 ]. UAVs are low-cost and easy-to-operate technologies in agriculture [ 83 ] that can be adopted for spraying in fields [ 84 ], such as water precision irrigation.…”

Drone-Based Mapping for Visual Identification of Sembiran Village Settlement Characteristics

“…a general workflow is to be followed to process UAV imagery using machine learning and statistical modes. The workflow contains the following steps: (1) collecting UAV data, considering pre-flight preparation, mission planning, and system characteristics; (2) UAV data processing followed by image pre-processing and photogrammetry processing; (3) machine learning and statistical models, including classification and regression methods, according to the desired study goals, and the accuracy assessment of the final products [7]. In this study, the workflow data used were UAV data collection, UAV data processing, and Settlement Pattern Analysis based on mapping results (figure 3).…”

“…Before an actual UAV flight, four major phases should be considered, namely UAV regulations, study area characteristics, weather conditions, and field data collection. These phases are essential for accurate UAV data collection and lead to a safely operated survey without collection mistakes [7]. Data collection in this study used DJI Phantom 4 Pro and processed with PIX4DMapper and ArcGIS software.…”

Large-Scale Reality Modeling of a University Campus Using Combined UAV and Terrestrial Photogrammetry for Historical Preservation and Practical Use

“…Examples of unsupervised machine learning with UAV photogrammetry include analyzing photos of plants (more on object recognition after explaining NBV), agricultural models, and forestry management [63][64][65]. NBV appears in recent publications such as Bolourian and Hammad [66], Ashour et al [67], and Almadhoun et al [68].…”