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Study and classification of plum varieties using image analysis and deep learning techniques

机译:使用图像分析和深层学习技术研究与分类梅花品种

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Currently much of the pre-harvest fruit valuation is still done by farmers or technicians that visually inspect the pieces of fruit. However, this process has great limitations since their decisions have high subjectivity and a thorough analysis of the whole production, or even a significant part of it, is unapproachable. Therefore, computer vision and machine learning techniques are increasingly being introduced into this process. In this work, we deal with the problem of automatically identifying plum varieties at early maturity stages, which is even difficult for the human expert. To face that identification, we propose a two-step procedure. Firstly, captured images are processed to identify the region where the plum appears. Secondly, we determine the plum variety using a deep convolutional neural network. Experimental results show that the proposed system achieves a remarkable behavior, with accuracy values that range from 91 to 97%.
机译:目前,农民或技术人员仍然由目前检查水果的农民或技术人员的大部分估值。 然而,这个过程具有很大的限制,因为他们的决定具有高主观性和对整个生产的彻底分析,甚至是其中的重要部分,是不可接定的。 因此,越来越多地引入计算机视觉和机器学习技术。 在这项工作中,我们处理在早期成熟阶段自动识别梅花品种的问题,这对人类专家甚至难以困难。 要面对识别,我们提出了一个两步的程序。 首先,处理捕获的图像以识别出现李子的区域。 其次,我们使用深卷积神经网络确定梅花。 实验结果表明,该系统实现了显着的行为,精度值范围为91至97%。

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