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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$\times$ 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$\times$ than the first model but it is still about one-third of the VGG-16 parameters.

A large training sample is prerequisite for the successful training of each deep learning model for image classification. Collecting a large dataset is time-consuming and costly, especially for plants. When a large dataset is not available, the challenge is how to use a small or medium size dataset to train a deep model optimally. To overcome this challenge, a novel model is proposed to use the available small size plant dataset efficiently. This model focuses on data augmentation and aims to improve the learning accuracy by oversampling the dataset through representative image patches. To extract the relevant patches, ORB key points are detected in the training images and then image patches are extracted using an innovative algorithm. The extracted ORB image patches are used for dataset augmentation to avoid overfitting during the training phase. The proposed model is implemented using convolutional neural layers, where its structure is based on ResNet architecture. The proposed model is evaluated on a challenging ACHENY dataset. ACHENY is a Chenopodiaceae plant dataset, comprising 27030 images from 30 classes. The experimental results show that the patch-based strategy outperforms the classification accuracy achieved by traditional deep models by 9%.

A common feature of photosynthesis in practically all organisms is the assimilation of CO₂ into organic matter via a catalyst called ribulose 1,5-bisphosphate carboxylase oxygenase (Rubisco) in the carbon assimilation cycle. One of the constraints on the process in terrestrial plants is conditions where CO₂ becomes limiting because of high temperature, drought, or soil salinity. This can occur by restricting the entry of CO₂ into leaves, by decreased stomatal conductance, by decreased cytoplasmic solubility of CO₂, and by increased photorespiration (a process resulting from O₂ competing with CO₂ in Rubisco catalysis). In response to CO₂ limitations, some terrestrial plants evolved mechanisms to concentrate CO₂ around Rubisco through a C₄ cycle that requires spatial separation of fixation of atmospheric CO₂ into C₄ acids, and the donation of CO₂ from C₄ acids via decarboxylases to Rubisco (called C₄ plants). The paradigm for C₄ photosynthesis in terrestrial plants for more than 35 years was that a dual-cell system, called Kranz leaf anatomy, is required for spatial separation of these functions. Surprisingly, recent research on species in family Chenopodiaceae has shown that C₄ photosynthesis can occur within a single photosynthetic cell. Two very novel means of accomplishing this evolved in subfamily Suaedoideae. These systems function by spatial development of two cytoplasmic domains, which contain dimorphic chloroplasts. Emerging information on the biochemical and structural strategies for accomplishing C₄ has promise for improving the productivity of rice, which lacks a CO₂-concentrating mechanism, and for securing this important crop as a food supply under CO₂-limited conditions predicted with global warming.