Aiming at the difficulty in identifying potato early blight and late blight, this paper conducts a disease classification study based on Xception. Due to the similar initial symptoms of the two diseases, the traditional expert identification method has many limitations, while accurate identification is crucial for disease prevention and control. The research takes relevant leaves as the object, adopts the "Potato Disease Leaf Dataset v1", which is divided into training set, validation set and test set. The model is optimized through four groups of comparative experiments.
| Mualliflar | Zhang, Hongzhi |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-09-17 |
| Son | 3 |
| Betlar | 23-28 |
| Til | Rus |
Potato; Disease leaves; Convolutional Neural Network; Xception; Dataset
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