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 notewor-thy 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 assess-ments. 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 effi-ciently and accurately for larger area monitoring operation.