Intelligent Fruit Detection System Using Optimized Hybrid Deep Learning Models

Accurate and efficient detection of dragon fruit ripeness is crucial for optimizing harvesting schedules, reducing post-harvest losses, and ensuring fruit quality. This research investigates applying optimized hybrid deep learning (DL) models for intelligent dragon fruit ripeness classification using a dataset of 2,563 images. The feature extraction using pre-trained CNNs, specifically DenseNet-50 and ResNet-50, followed by dimensionality reduction using Principal Component Analysis (PCA). The reduced feature sets are then fed into various classifiers, including Support Vector Machines (SVM) with linear and RBF kernels, a Voting ensemble of SVMs, and a Multi-Layer Perceptron (MLP). The performance of models is evaluated using key metrics such as accuracy, AUC, etc. The experimental findings indicate that the DenseNet-50 features combined with PCA and an SVM Voting ensemble achieve the highest classification accuracy of 97.71%, along with a balanced recall, precision, and F1-score of 0.96. The ResNet-50 features coupled with an MLP also exhibit competitive performance.