We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach.
- Authors
Terlapu, Panduranga Vital; Prasan, U D; Kumar, T. Ravi; Bendalam, Vijaya; Pappu, Sasibhushana Rao; Rao, M. Jayanthi; Mohitha, Maddula Ratna; D., Jayaram
- Abstract
Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis.
- Subjects
DEEP learning; RICE quality; MACHINE learning; RICE; AGRICULTURE; SUPPORT vector machines; IMAGE analysis
- Publication
IAENG International Journal of Computer Science, 2024, Vol 51, Issue 7, p765
- ISSN
1819-656X
- Publication type
Article