# Anatomical study and early diagnosis of dome galls in Cordia Dichotoma using DeepSVM model

## TL;DR

This paper introduces a new AI model called DeepSVM to detect dome galls in Cordia dichotoma leaves, achieving high accuracy for automated plant disease monitoring.

## Contribution

The novel DeepSVM model combines deep learning with SVM to improve training stability and generalization for plant disease detection.

## Key findings

- The DeepSVM model achieved 94.50% accuracy and 94.47% F1-score in detecting dome galls.
- DeepSVM outperformed models like VGG-16, InceptionResNetv2, and MobileNet-V2 in stability and accuracy.
- The hybrid model improved robustness and reduced overfitting compared to traditional CNNs.

## Abstract

Artificial intelligence (AI), particularly deep learning (DL), offers automated solutions for early detection of plant diseases to improve crop yield. However, training accurate models on real-field data remains challenging due to over fitting and limited generalization. As observed in prior studies, traditional CNNs often struggle with real-environment variability, and transfer learning can lead to instability in training on domain-specific leaf datasets. This study focuses on detecting dome galls, a disease in Cordia dichotoma, by formulating a binary classification task (healthy vs. diseased leaves) using a custom dataset of 3,900 leaf images collected from real field environments.

Initially, both custom CNNs and transfer learning models were trained and compared. Among them, a modified ResNet-50 architecture showed promising results but suffered from over fitting and unstable convergence. To address this, the final sigmoid activation layer was replaced with a Support Vector Machine (SVM), and L2 regularization was applied to reduce over fitting. This hybrid DeepSVM architecture stabilized training and improved model robustness. Image preprocessing and augmentation techniques were applied to increase variability and prevent over fitting.

The final model was evaluated on a separate test set of 400 images, and the results remained stable across repeated runs. DeepSVM achieved an accuracy of 94.50% and an F1-score of 94.47%, outperforming other well-known models like VGG-16, InceptionResNetv2, and MobileNet-V2.

These results indicate that the proposed DeepSVM approach offers better generalization and training stability than conventional CNN classifiers, potentially aiding in automated disease monitoring for precision agriculture.