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

DOI:10.21548/31-2-1402 期刊:South African Journal of Enology and Viticulture 出版年份:2016 更新时间:2025-09-23 15:21:01
摘要: 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.
作者: J.L. Smit,G. Sithole,A.E. Strever
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To assess vine biomass differences using aerial imagery as the primary source of data for vine analysis, automatically identify vineyards blocks, vine rows, and individual vines within rows, and determine the correlation of image data with the biophysical data (yield and pruning mass) of each vine.

The segmentation technique developed in this study reliably detects vine rows in aerial imagery, improving previous classification methods and leading to better estimators of vine biophysical measures. The method can detect curved vine rows and uses both RGB and NIR images, offering advantages over other techniques. Future work will explore the correlation between vegetation indices and vine biophysical measures in more detail and apply the techniques to different vineyards.

The method is sensitive to shadows cast by trees and overhanging trees, which can cause adjacent vine rows to be combined into the same segment. The segmentation does not account for the spatial and spectral features of a vine, leading to poor correlations in some cases. Future work will focus on using RGB images for better discrimination of overgrown inter-rows and implementing a hierarchical segmentation approach.

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