A new methodology for estimating the grapevine-berry number per cluster using image analysis
A new methodology for estimating the grapevine-berry number per cluster using image analysis
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DOI：
10.1016/j.biosystemseng.2016.12.011
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发表时间：
2017-04-01
影响因子：
5.1
通讯作者：
Tardaguila, Javier
中科院分区：
文献类型：
--
作者：
Aquino, Arturo;Diago, Maria P.;Tardaguila, Javier
本文提出了一种基于数学形态学和像素分类的葡萄浆果计数新图像分析算法。首先，提取出一组由连通分量表示的浆果候选对象。然后，利用这些分量的关键特征计算出六个描述符，并采用监督方法用于假阳性（FP）判别。更具体地说，这组描述符模拟了葡萄独特的形状、光反射模式和颜色。测试了两种分类器，一个三层神经网络和一个优化的支持向量机。使用低成本智能手机相机采集了152张图像的数据集。图像来自7个葡萄品种，每个品种18张，处于巴焦利尼量表中浆果坐果（称为K阶段；94张图像）和果穗闭合（称为L阶段；32张图像）之间的两个物候阶段。其中126张图像用于外部验证，其余26张用于训练（L阶段12张，K阶段14张）。从这些训练图像中，根据六个描述符生成并标记了5438个真/假阳性样本。神经网络的性能优于支持向量机，其召回率和准确率的平均值分别稳定在0.9572和0.8705。所提出的算法作为智能手机应用程序实现，可为葡萄和葡萄酒行业的田间无损产量预测和浆果坐果评估提供有用的诊断工具。（C）2017英国农业工程师学会。由爱思唯尔有限公司出版。保留所有权利。
A new image analysis algorithm based on mathematical morphology and pixel classification for grapevine berry counting is presented in this paper. First, a set of berry candidates represented by connected components was extracted. Then, six descriptors were calculated using key features of these components, and were employed for false positive (FP) discrimination using a supervised approach. More specifically, the set of descriptors modelled the grapes' distinctive shape, light reflection pattern and colour. Two classifiers were tested, a three-layer neural network and an optimised support vector machine. A dataset of 152 images was acquired with a low-cost smart phone camera. Images came from seven grapevine varieties, 18 per variety, at the two phenological stages in the Baggiolini scale between berry set (named stage K; 94 images) and cluster-closure (named stage L; 32 images). 126 of these images were kept for external validation and the remaining 26 were used for training (12 at stage L and 14 at K). From these training images, 5438 true/false positive samples were generated and labelled in terms of the six descriptors. The neural network performed better than the support vector machine, yielding consistent Recall and Precision average values of 0.9572 and 0.8705, respectively.The presented algorithm, implemented as a smartphone application, can constitute a useful diagnosis tool for the in-the-field and non-destructive yield prediction and berry set assessing for the grape and wine industry. (C) 2017 IAgrE. Published by Elsevier Ltd. All rights reserved.