Vine Signal Extraction – an Application of Remote Sensing in Precision Viticulture

Abstract

This paper presents a study of precision agriculture in the wine industry. While precision viticulture mostly aims to maximise yields by delivering the right inputs to appropriate places on a farm in the correct doses and at the right time, the objective of this study was rather to assess vine biomass differences. The solution proposed in this paper uses aerial imagery as the primary source of data for vine analysis. The first objective to be achieved by the solution is to automatically identify vineyards blocks, vine rows, and individual vines within rows. This is made possible through a series of enhancements and hierarchical segmentations of the aerial images. The second objective is to determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine. A multispectral aerial image is used to compute vegetation indices, which serve as indicators of biophysical measures. The results of the automatic detection are compared against a test field, to verify both vine location and vegetation index correlation with relevant vine parameters. The advantage of this technique is that it functions in environments where active cover crop growth between vines is evident and where variable vine canopy conditions are present within a vineyard block.

Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure

“…A number of studies explored aerial imagery with resolutions of around few decimeters to map crop vigor [3,[13][14][15] and grape quality [16]. Most of them are based on the normalized vegetation index (NDVI) proposed by [17].…”

Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard

“…These indices are based in the fact that healthy, vigorous vines will exhibit strong near-infrared reflectance and very low reflectance in the visible region of the spectrum [1,8]. Once the VI have been calculated, they are classified into a pseudo-color index images, whereby distinct color classes represent manageable differences in vine variability [9]. The use of VI maps has proven to be an invaluable tool to viticulturists interested in evaluating spatial variability in canopy vigor and subsequent crop performance [10].…”

“…[14]. Therefore, for the construction of accurate vineyard maps, all non-vine row vegetation needs to be identified and removed to aid in the accurate estimation of plant biophysical parameters [9,14,17].…”

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

“…In these systems, despite the high spatial resolution of the sensors currently employed, the outcoming information, such as the vigour zoning, only accounts for averaged data neglecting the contribution of single vine (Arnó, Martínez Casasnovas, Ribes Dasi, & Rosell, 2009). While row detection techniques saw a great development in these last few years (Comba, Gay, Primicerio, & Aimonino, 2015;Delenne, Durrieu, Rabatel, & Deshayes, 2010;Puletti, Perria, & Storchi, 2014;Smit, Sithole, & Strever, 2010), a methodology for single plant detection is still not available. Instead, the ability to recognize automatically single vine within a training row could remarkably improve the representation of the contribution of single plant to the canopy curtain, enabling to detect specific plant pathologies in the row and improving the accuracy of vigour zoning (Lee et al, 2010;Naidu, Perry, Pierce, & Mekuria, 2009;Sankaran, Mishra, Ehsani, & Davis, 2010).…”