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. To evaluate our models, their performance was compared with the well-known VGG-16 model after fine-tuning it on ACHENY. The experimental results show that the two proposed models can do Chenopodiaceae fine-grained species recognition with promising accuracy. Second, a model based on the EfficientNet-B1 architecture, which is pre-trained on ImageNet and is fine-tuned on ACHENY. 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. The other goal of this study is to investigate the applicability of ACHENY dataset by using deep learning models. This dataset is called ACHENY and contains 27030 images of 30 Chenopodiaceae species in their natural habitats. Hence, the main goal of this research is to provide a standard dataset of Chenopodiaceae images. A standard dataset is essential in order to perform a deep learning algorithm. Modern computer vision approaches are based on deep learning techniques. Computer vision approaches can be used for automatic species analysis. 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. The Chenopodiaceae species are ecologically and financially important, and play a significant role in biodiversity around the world.

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