Abstract
Off-type plants, or other distinct varieties of the same plant, need to be detected and removed at early stage from the fields to preserve the genetic integrity and significant traits of a given plant variety. At present, these off-type plants are manually identified in the cultivation fields through morphological analysis of leaves. This method is time-consuming and involves skilled labor to define the off-type plant. Hence, masking technique based attention mechanism is proposed for identifying the off-type from the leaf images captured in the cultivation fields. The proposed method recognizes the plant variety by locating and visualizing the prominent parts of the leaf which is similar to the human visual attention mechanism. When the prominent parts of the leaf image are masked, there is a significant drop in the class probability of the convolutional network. This reflects the importance of such prominent parts which directly account for its variety. An attention module is designed using neural networks which focuses on such significant regions and improves the performance of the network. The proposed method is tested on a field image dataset consisting of 1235 images of Sunflower leaves and achieves the highest mean accuracy of 98.25% in identifying the plant variety.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.