Uncertainty sources affecting operational efficiency of ML algorithms in UAV-based precision agriculture: A 2013–2020 systematic review

Abstract

Conventional methods of data sampling in agriculture are time consuming, labor intensive, destructive, subject to human error and affected by field conditions. Thus, remote sensing technologies such as unmanned aerial vehicles (UAVs) became widely used as an alternative for data collection. Nevertheless, the big data captured by the UAVs is challenging to interpret. Therefore, machine learning algorithms (MLs) are used to interpret this data. However, the operational efficiency of those MLs is yet to be improved due to different sources affecting their modeling certainty. Therefore, this study aims to review different sources affecting the accuracy of MLs regression and classification interventions in precision agriculture. In this regard, 109 articles were identified in the Scopus database. The search was restricted to articles written in English, published during 2013–2020, and used UAVs as in-field data collection tools and ML algorithms for data analysis and interpretation. This systematic review will be the point of review for researchers to recognize the possible sources affecting the certainty of regression and classification results associated with MLs use. The recognition of those sources points out areas for improvement of MLs performance in precision agriculture. In this review, the performance of MLs is still evaluated in general, which opens the road for further detailed research.