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Comparison of the TrIncNet model’s performance with a recent research work present in the literature for the identification of Maize plant diseases.
| Research Work | Techniques used | Dataset used | Type of dataset | Testing Accuracy | Number of trainable weight parameters (In millions) |
|---|---|---|---|---|---|
| (Haque et al., 2022) | InceptionV3 with Global Average Pooling layer | Maize dataset | Captured from field | 95.99% | 21.78 |
| Proposed Work | TrIncNet model | Maize dataset | Captured from field | 96.93% | 6.95 |
