HIGHLIGHTS
- What: The model was trained and tested on 3200 manually collected rice samples from four categories, ultimately demonstrating robust performance with a test accuracy of 95.31%. The authors propose a Deep Ensemble Convolutional Neural_Network (DECNN) model consisting of three modified transfer learning (TL) models for the high-precision classification of sugarcane leaf diseases. In this study, a dataset related to sugarcane leaf diseases was obtained from Kaggle . The aim of this approach is to enhance the model`s generalization capabilities, particularly in highdimensional datasets.
- Who: Kaiwen Hu et al. from the College of Computer . . .
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