Image Classification of Vegetable Quality using Support Vector Machine based on Convolutional Neural Network

As part of an effort to develop intelligent agriculture, new methods for enhancing the quality vegetables are being continually developed. In recent years, Convolutional Neural Network (CNN) has shown be most successful and extensively used approach identifying pre-trained vegetables. However, this method is time-consuming due scarcity truly large, significant datasets. Using a CNN model as feature extractor straightforward utilizing CNNs' capabilities without investing time in training. While, Support Vector Machine (SVM excels at processing data with tiny dimensions significantly larger instances. SVM more accurately classifies flatten/vector supplied by fully connected layer small dimensions. addition, implementing Data Augmentation (DA) Weighted Class (WC) variety class imbalance reduction can improve CNN-SVM performance. The research results show highest accuracy during training always achieves 100% across all experimental options. With average 69.66% testing process 92.51% prediction data, findings demonstrate that outperforms terms performance possible experiments, or WC DA approach.