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首页> 外文期刊>Ecological informatics: an international journal on ecoinformatics and computational ecology >Columnar cactus recognition in aerial images using a deep learning approach
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Columnar cactus recognition in aerial images using a deep learning approach

机译:使用深度学习方法在空中图像中的柱状仙人掌识别

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摘要

Tehuacan-Cuicatlan Valley is a semi-arid zone in the south of Mexico. It was inscribed in the World Heritage List by the UNESCO in 2018. This unique area has wide biodiversity including several endemic plants. Unfortunately, human activity is constantly affecting the area. A way to preserve a protected area is to carry out autonomous surveillance of the area. A first step to reach this autonomy is to automatically detect and recognize elements in the area. In this work, we present a deep learning based approach for columnar cactus recognition, specifically, the Neobuxbaumia tetetzo species, endemic of the Valley. An image dataset was generated for this study by our research team, containing more than 10,000 image examples. The proposed approach uses this dataset to train a modified LeNet-5 Convolutional Neural Network. Experimental results have shown a high recognition accuracy, 0.95 for the validation set, validating the use of the approach for columnar cactus recognition.
机译:Tehuacan-Cuicatlan山谷是墨西哥南部的半干旱区。 它在2018年由联合国教科文组织在世界遗产名录中刻字。这个独特的地区具有广泛的生物多样性,包括几种地方性植物。 不幸的是,人类活动不断影响该地区。 一种保护保护区的方法是开展该地区的自主监控。 达到这种自主权的第一步是自动检测和识别该区域中的元素。 在这项工作中,我们提出了一种基于深度学习的柱状仙人掌识别方法,具体而言,Neobuxbaumia Tetzo物种,流域的流动。 我们的研究团队为本研究生成了一个图像数据集,包含超过10,000个图像示例。 所提出的方法使用此数据集培训修改的Lenet-5卷积神经网络。 实验结果显示了验证集的高识别精度,0.95,验证了柱状仙人掌识别方法的使用。

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