Abstract
Leaves images of diseased and healthy plants are play an important role in the plant disease detection and diagnosis. This study proposed a Gradient Boosting Decision Tree (GBDT) method for corn disease identification. Firstly, we use Synthetic Minority Over-Sampling Technique (SMOST) method to solve the problem of data imbalance. Then, we select regional interpolation algorithm to resize the leaf image resolution to 30×20. Finally, gradient boosting decision tree algorithm is employed to corn disease identification. The experiment results on Northern Leaf Blight-Infected dataset indicated that the proposed method is effective, and achieves 92.5% accuracy.
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 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.
