LeafGuard: Efficient Soybean Leaf Defect Classification in Indian Agriculture Using Fine-tuned CNN
Keywords:
Soybean, VGGNet, ResNet, Xception, Inception, CNN, Leaky ReLUAbstract
Soybean leaf diseases have a significant impact on people's lives. With numerous distinct ailments, the process of detecting and categorizing them using artificial vision is both time-consuming and labor-intensive, leading to an increased risk of errors. While transfer learning-based algorithms have shown promising results in image categorization, they often struggle to extract all essential information, resulting in a decline in categorization accuracy. In this study, we propose a transfer learning-based approach to identify and classify soybean leaf diseases. We investigate how the performance of a classification system is influenced by the dataset size, emphasizing that a broader perspective can significantly enhance accuracy. To improve the categorization of soybean photos, we employ image creation techniques using well-known architectures such as VGG16-19, ResNet50, Xception, and InceptionV3. In our methodology, we incorporate Conv2d kernels and Leaky ReLU layers to reduce model parameters, resulting in improved efficiency. Our experimental results demonstrate that our model outperforms standard transfer learning networks on the test set, achieving a reduction in both parameters and training time. These findings affirm the effectiveness of our technique in the classification of soybean leaf diseases, offering a potential solution to the challenges posed by these diverse ailments.
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