Abstract
Automatic recognition of desert plant types by machine vision can support the research on wind prevention and sand fixation, ecosystem value assessment, vegetation restoration and reconstruction, and reduce the dependence on plant expert identification. At present, the research on the machine discrimination model of desert plants mainly relies on the standardized high-quality plant specimen images, lacking the desert plant images obtained under complex natural conditions. This dataset provides typical desert plant images of Xinjiang that can be used for the model training of deep learning image classification, including 15,550 digital camera images of desert plants in Xinjiang obtained under different seasons, natural backgrounds and lighting conditions, and covering 19 typical desert plant types. Suaeda salsa has the smallest number of images and Artemisia desertorum has the biggest, 465 and 1,240 respectively, with a median of 800, which has met the training needs of mainstream deep learning model. This dataset can provide basic data for desert plant image segmentation, target detection and automatic recognition.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
