Journal Article

Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

Tornambè, Calogero, Muratore, Francesco, Cantini, Claudio, Avola, Giovanni, Riggi, Ezio, Matese, Alessandro, Di Gennaro, Salvatore Filippo

2019

Overview

The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green520–600, red630–690 and near-infrared760–900 bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis—PCA and linear discriminant analysis—LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases.

• Publisher: MDPI AG
• Subject: Agriculture / analysis of variance / Cultivars / Discriminant analysis / Discrimination / Image processing / Image segmentation / leaf reflectance / Olea europaea / Olea europaea L., canopy / olives / precision agriculture / Principal components analysis / Reflectance / Remote sensing / Rootstocks / Scions / Software / Spectrum analysis / Sugarcane / unmanned aerial vehicle (UAV), vegetation indices (VIs), cultivar recognition / Unmanned aerial vehicles / Variance analysis / Vegetation