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.
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