The Chenopodiaceae species are ecologically and financially important, and play a significant role in biodiversity around the world. Biodiversity protection is critical for the survival and sustainability of each ecosystem and since plant species recognition in their natural habitats is the first process in plant diversity protection, an automatic species classification in the wild would greatly help the species analysis and consequently biodiversity protection on earth. Computer vision approaches can be used for automatic species analysis. Modern computer vision approaches are based on deep learning techniques. A standard dataset is essential in order to perform a deep learning algorithm. Hence, the main goal of this research is to provide a standard dataset of Chenopodiaceae images. This dataset is called ACHENY and contains 27030 images of 30 Chenopodiaceae species in their natural habitats. The other goal of this study is to investigate the applicability of ACHENY dataset by using deep learning models. Therefore, two novel deep learning models based on ACHENY dataset are introduced: First, a lightweight deep model which is trained from scratch and is designed innovatively to be agile and fast. Second, a model based on the EfficientNet-B1 architecture, which is pre-trained on ImageNet and is fine-tuned on ACHENY. The experimental results show that the two proposed models can do Chenopodiaceae fine-grained species recognition with promising accuracy. To evaluate our models, their performance was compared with the well-known VGG-16 model after fine-tuning it on ACHENY. Both VGG-16 and our first model achieved about 80% accuracy while the size of VGG-16 is about 16× larger than the first model. Our second model has an accuracy of about 90% and outperforms the other models where its number of parameters is 5× than the first model but it is still about one-third of the VGG-16 parameters.

中文翻译：

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野生藜科分类的高效深度学习模型

这藜科物种在生态和经济上都很重要，并且在世界各地的生物多样性中发挥着重要作用。生物多样性保护对于每个生态系统的生存和可持续性都至关重要，由于自然栖息地中的植物物种识别是植物多样性保护的第一个过程，因此在野外自动进行物种分类将极大地帮助物种分析，从而有助于地球上的生物多样性保护。计算机视觉方法可用于自动物种分析。现代计算机视觉方法基于深度学习技术。为了执行深度学习算法，标准数据集是必不可少的。因此，本研究的主要目标是提供一个标准的数据集藜科图片。该数据集称为 ACHENY，包含 27030 张图像，共 30藜科物种在其自然栖息地。本研究的另一个目标是通过使用深度学习模型来研究 ACHENY 数据集的适用性。因此，介绍了两种基于 ACHENY 数据集的新型深度学习模型：首先，一种轻量级的深度模型，从头开始训练，创新设计为敏捷和快速。二是基于 EfficientNet-B1 架构的模型，在 ImageNet 上进行预训练，在 ACHENY 上进行微调。实验结果表明，所提出的两种模型都可以藜科具有良好准确性的细粒度物种识别。为了评估我们的模型，在 ACHENY 上对其进行微调后，将它们的性能与著名的 VGG-16 模型进行了比较。VGG-16 和我们的第一个模型都达到了大约 80% 的准确率，而 VGG-16 的大小约为 16×比第一个模型大。我们的第二个模型的准确率约为 90%，并且优于其他参数数量为 5 的模型×比第一个模型，但它仍然是 VGG-16 参数的三分之一左右。