Year: 2025
Type: article
Abstract: 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.
Source: Journal of Machine and Computing
Authors: Angajala Guna Sai Abhishek, Ravi Kumar T, Panduranga Vital Terlapu, Chalapathi Rao Tippana, Ramkishor Pondreti
Institution: Aditya Institute of Technology and Management
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4411896836