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Synergistic Use of Convolutional Neural Networks and Support Vector Machines for Mango Leaf Disease Diagnosis

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Analysis demonstrates the hybrid approach improves disease detection in mangoes, suggesting better crop yields.

The hybrid CNN-SVM model achieved a remarkable accuracy of 99.75% in diagnosing mango leaf diseases, outperforming traditional methods.
Using a dataset of 4000 images, the study analyzed eight mango leaf conditions, including Anthracnose and Powdery Mildew.
The proposed model applies rigorous pre-processing and data augmentation techniques to enhance its generalizability and robustness.
This research supports the integration of deep learning and machine learning for sustainable agricultural practices and improved productivity.

Salma et al. (2025) studied this question.

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View Full Paper

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Authors

Analysis demonstrates the hybrid approach improves disease detection in mangoes, suggesting better crop yields.

The hybrid CNN-SVM model achieved a remarkable accuracy of 99.75% in diagnosing mango leaf diseases, outperforming traditional methods.
Using a dataset of 4000 images, the study analyzed eight mango leaf conditions, including Anthracnose and Powdery Mildew.
The proposed model applies rigorous pre-processing and data augmentation techniques to enhance its generalizability and robustness.
This research supports the integration of deep learning and machine learning for sustainable agricultural practices and improved productivity.

Salma et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark