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A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis

机译:通过图像分析对葡萄浆果和其他水果进行花梗/花梗检测和大小评估的新方法

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摘要

The berry size of wine-grapes has often been considered to influence wine composition and quality, as it is related to the skin-to-pulp ratio of the berry and the concentration of skin-located compounds that play a key role in the wine quality. The size and weight of wine-grapes are usually measured by hand, making it a slow, tedious and inaccurate process. This paper focuses on two main objectives aimed at automating this process using image analysis: (1) to develop a fast and accurate method for detecting and removing the pedicel in images of berries, and (2) to accurately determine the size and weight of the berry. A method to detect the peduncle of fruits is presented based on a novel signature of the contour. This method has been developed specifically for grapevine berries, and was later extended and tested with an independent set of other fruits with different shapes and sizes such as peppers, pears, apples or mandarins. Using this approach, the system has been capable of correctly estimating the berry weight (R2 > 0.96) and size (R2 > 0.97) of wine-grapes and of assessing the size of other fruits like mandarins, apples, pears and red peppers (R2 > 0.93). The proven performance of the image analysis methodology developed may be easily implemented in automated inspection systems to accurately estimate the weight of a wide range of fruits including wine-grapes. In this case, the implementation of this system on sorting tables after de-stemming may provide the winemaker with very useful information about the potential quality of the wine. © 2013 IAgrE.
机译:葡萄皮的浆果大小通常被认为会影响葡萄酒的成分和质量,因为它与浆果的皮浆比以及在葡萄酒质量中起关键作用的皮肤定位化合物的浓度有关。 。葡萄的大小和重量通常是手工测量的,这是一个缓慢,繁琐和不准确的过程。本文着重于两个主要目标,旨在利用图像分析实现此过程的自动化:(1)开发一种快速,准确的方法来检测和去除浆果图像中的花梗,(2)准确确定果壳的大小和重量浆果。提出了一种基于轮廓新颖特征的水果花梗检测方法。该方法是专门为葡萄浆果开发的,后来被扩展并通过一组独立的其他形状和大小不同的水果(例如胡椒,梨,苹果或普通话)进行了测试。使用这种方法,该系统能够正确估计葡萄酒葡萄的浆果重量(R2> 0.96)和大小(R2> 0.97),并能够评估其他水果的大小,例如橘子,苹果,梨和红辣椒(R2 > 0.93)。所开发的图像分析方法论的成熟性能可以在自动检查系统中轻松实现,以准确估算包括葡萄柚在内的多种水果的重量。在这种情况下,该系统在去梗后在分拣台上的实施可能会为酿酒师提供有关葡萄酒潜在品质的非常有用的信息。 ©2013 IAgrE。

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