| Abstrakt: |
Paddy is a major nutritional requirement for the human population. However, diseases such as bacterial leaf blight, brown spot, and leaf smut are significantly impacting the leaves of the paddy at various stages, resulting in significant losses. Consequently, this leads to an important decrease in production. An effective approach would involve assigning agricultural specialists to the affected area to provide remedies for the affected crops. This task is both time-consuming and tedious. The latest technological advancements will provide alternative solutions in a more efficient and quicker manner. We employed a deep transfer learning algorithm to assess the accuracy of six frequently utilised transfer learning algorithms. In order to improve the accuracy of the model, we boosted the quality of the images using adaptive filtering and Gaussian Mixture Model (GMM) clustering technique. This allows for the simple segmentation of images into diseased, normal, and surrounding regions, resulting in more accurate classification. We applied a deep transfer learning method to evaluate the accuracy of six widely used transfer learning algorithms, specifically VGG, ResNet, Inception, MobileNet, DenseNet, and EfficientNet. The DenseNet model achieved an average accuracy of 94%, with a precision of 92%, recall of 95%, and an F1-score of 93%. To improve performance, we implemented an ensemble technique, which involves combining two or more models. The Ensemble Convolutional Neural Network (ECNN) was designed by using three pre-trained architectures: ResNet, DenseNet, and EfficientNet.The ensemble model's final predict was determined by employing a weighted average methodology to enhance the prediction's accuracy. This technique shows a perfect balance between performance and accuracy in diagnosing diseases in paddy leaves. [ABSTRACT FROM AUTHOR] |
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