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.

Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps

“…The impact of data fusion varies with the type of application. Studies that focused on the fusion of digital images invariably showed improvements with respect to the results obtained using single data sources [ 18 , 19 , 39 , 84 , 99 , 106 ], although in some cases the improvement may not be substantial enough to justify the capture of additional data [ 21 , 71 ]. The success of data fusion applied to digital images can be linked not only to the complementarities shown by different types of images, but also to the fact that the images can be easily made compatible using simple normalization operations when needed.…”

“…In their work, the model was tested using data collected in a different year, under the justification that validating a model with data captured under very similar conditions to those used for training will almost invariably lead to unrealistic results, which unfortunately is usually the case. Indeed, when this approach was applied by Zhou et al [ 71 ], the results obtained for different years were strongly disparate, thus revealing that the data used for training was not representative enough. Veum et al [ 36 ] added that it is difficult to find the ideal sample distribution due to too much homogeneity and presence of extraneous factors.…”

“…Satellite data are used for mapping variables as diverse as soil moisture [ 42 , 43 , 44 ], crop type [ 45 , 46 , 47 , 48 , 49 , 50 ], crop phenological states [ 51 , 52 ], evapotranspiration [ 40 , 53 , 54 , 55 , 56 , 57 , 58 ], nitrogen status [ 59 , 60 , 61 , 62 ], biomass [ 63 , 64 ], among others. While most data fusion approaches only use data in the same scale, a few studies have applied data originating from different scales [ 10 , 26 , 28 , 31 , 38 , 40 , 51 , 52 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ].…”

Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery

“…Multiple linear regression (MLR) is a statistical technique that predicts the outcome of a dependent variable using multiple independent variables. The RF Regression is a supervised learning approach for regression that uses the ensemble learning technique for remote sensingbased agricultural research projects [17,37]. The decision tree (DT) uses a tree topology to generate regression or classification models.…”

“…ML technologies have been used to predict crop parameters [37]. For example, winter wheat biomass estimation was carried out using the visualization approach for SVR and the investigation of influential textures [39].…”