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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 21; p. 3511
• Main Authors: Eskandari, Roghieh, Mahdianpari, Masoud, Mohammadimanesh, Fariba, Salehi, Bahram, Brisco, Brian, Homayouni, Saeid
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26.10.2020
• Subjects: Accuracy; Aerial photography; Agriculture; agro-environmental monitoring; Aircraft; Algorithms; area; artificial intelligence; Classification; cost effectiveness; Data collection; Data processing; decision support systems; Environmental monitoring; Environmental studies; Forestry; Grasslands; image analysis; Imagery; Learning algorithms; Machine learning; Mathematical models; Meta-analysis; Model accuracy; monitoring; Photogrammetry; regression; Regression analysis; Regression models; Remote sensing; researchers; Reviews; Sensors; Software; Spatial analysis; spatial data; Spatial discrimination; Spatial resolution; Statistical analysis; Statistical models; Systematic review; Unmanned Aerial Vehicle (UAV); Unmanned aerial vehicles; Vegetation mapping; Wetlands
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12213511

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.