UAV Data as an Alternative to Field Sampling to Monitor Vineyards Using Machine Learning Based on UAV/Sentinel-2 Data Fusion

Abstract

Pests and diseases affect the yield and quality of grapes directly and engender noteworthy economic losses. Diagnosing “lesions” on vines as soon as possible and dynamically monitoring symptoms caused by pests and diseases at a larger scale are essential to pest control. This study has appraised the capabilities of high-resolution unmanned aerial vehicle (UAV) data as an alternative to manual field sampling to obtain sampling canopy sets and to supplement satellite-based monitoring using machine learning models including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme learning regression (ELR) with a new activation function. UAV data were acquired from two flights in Turpan to determine disease severity (DS) and disease incidence (DI) and compared with field visual assessments. The UAV-derived canopy structure including canopy height (CH) and vegetation fraction cover (VFC), as well as satellite-based spectral features calculated from Sentinel-2A/B data were analyzed to evaluate the potential of UAV data to replace manual sampling data and predict DI. It was found that SVR slightly outperformed the other methods with a root mean square error (RMSE) of 1.89%. Moreover, the combination of canopy structure (CS) and vegetation index (VIs) improved prediction accuracy compared with single-type features (RMSEcs of 2.86% and RMSEVIs of 1.93%). This study tested the ability of UAV sampling to replace manual sampling on a large scale and introduced opportunities and challenges of fusing different features to monitor vineyards using machine learning. Within this framework, disease incidence can be estimated efficiently and accurately for larger area monitoring operation.