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Image Classification Using Transfer Learning

Chandrika Lamani

Abstract


Grapevine diseases such as Black Rot, Black Measles, and Leaf Blight can quickly damage vineyards, reducing both yield and farmer income. Deep learning—especially CNNs—offers a strong way to automate disease detection, but training these models from scratch needs large datasets. Transfer learning solves this by reusing pre-trained networks for faster, more accurate classification. This study evaluates VGG16, ResNet101V2, DenseNet121, InceptionV3, Xception, and MobileNet for grapeleaf disease detection. Earlier work reports DenseNet121 reaching 99.67% accuracy [1]. Our analysis compares model behavior and training patterns, showing that transfer learning is a reliable and efficient approach for agricultural image diagnosis.


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References


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