Table 1 Comparison of various existing models for fruit disease detection
References | Method | Plant type | Edge computing | Bounding box prediction | Transfer learning | Accuracy % |
|---|---|---|---|---|---|---|
[1] | Machine learning with ANN | Plant village | No | No | No | 94.25 |
[2] | PCA | Apple leaves | Yes | No | No | 95.24 |
[3] | Random forest | Cassava leaves | No | No | No | 93.56 |
[4] | CNN model | Fruit classification | No | Yes | Yes | 91.24 |
[5] | Deep learning | Apple leave | Yes | No | Yes | 90.75 |
[6] | SVM with ANN | Cassava leaves | Yes | Yes | No | 94.56 |
[7] | Hybrid random forest | Cassava leaves | No | No | No | 95.33 |
Proposed model | Context data fusion with faster CNN | Citrus fruit disease | Yes | Yes | Yes | 98.96 |