From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data

Abstract

International audienceThe increasing availability of Very High Spatial Resolution images enables accurate digital maps production as an aid for management in the agricultural domain. In this study we develop a comprehensive and automatic tool for vineyard detection, delineation and characterizationusing aerial images and without any parcel plan availability. In France, vineyard training methods in rows or grids generate periodic patterns which make frequency analysis a suitable approach. The proposed method computes a Fast Fourier Transform on an aerial image, providing the delineation of vineyards and the accurate evaluation of row orientation and interrow width. These characteristics are then used to extract individual vine rows, with the aim of detecting missing vine plants and characterizing cultural practices. Using the red channel of an aerial image, 90\% of the parcels have been detected; 92\% have been correctly classified according to their rate of missing vine plants and 81\% according to their cultural practice (weed control method). The automatic process developed can be easily integrated into the final user's Geographical Information System and produces useful information for vineyard managemen

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

“…These quantitative results are similar or better than those that have been reported in the literature [8,9,23,24,28,30] for vineyards or similar crop detection studies. It should be also noted that in the EU (in particular, in Greece), the terroirs are significantly smaller in size, usually covering non-flat viticulture regions with highly complex terrain, and thus, their detection is significantly more challenging than, e.g., in Australia, California, Chile.…”

“…Regarding the developed canopy extraction methodology in contrast to similar efforts that are based on (e.g., NDVI) thresholds and/or line detection/fitting [28,32,49], we formulate the problem under a supervised object-based classification framework and propose a set of spectral and spatial features that can extract the canopy (not just lines, center-lines, etc.) while addressing different in-between the rows materials, like soil, weeds, rock, etc.…”

“…In particular, apart from the within-field crop analysis and the estimation of the spatial variability in wine-grape composition and yield [17][18][19][20][21][22][23], there is plenty of research towards the classification, identification and delineation of crops in remote sensing data [24][25][26][27]. However, despite recent research efforts towards the detection and delineation on medium [28][29][30] and high resolution [31,32] spatial scales, the development and validation of efficient classification frameworks for operational vineyard detection in high resolution data and over large agricultural regions still remain a challenge.…”

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

“…The key strengths of UAV are the high spatial ground resolution and a reduced planning time, which allows for highly flexible and timely vineyard monitoring [15,[38][39][40]. This study presents results using different classifications methods to detect and segment the vine canopy in ultra-high-resolution RGB imagery obtained from UAV.…”

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).…”