Abstract
As a key research in space science, plant experiments are persistently arranged and massive plant images are acquired, which are then almost manually processed for further analysis in tradition. This paper proposes a novel image segmentation method based on multi-scale deep feature fusion for automatic plant image segmentation. The method applies a deep convolutional neural network to extract multiple levels of features, and a skip architecture hierarchically fuses high-level to low-level features extracted by a deep layer, a middle layer and a shallow layer to recognize plant on the pixel level. The hierarchical feature fusion combines semantic information, middle-level information and geometrical features, which improves pixel-wise segmentation accuracy. Experiments demonstrate that our method performs well in segmentation accuracy, and can be applied to automatically extract useful information in space plant experiments.
Sign-in/Register to access full text options 
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 callMore From: DEStech Transactions on Computer Science and Engineering
Disclaimer: 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.
