Current - Issue
Original Article
Early Detection of Papaya and Mango Fruit Diseases Using Deep Learning
Nisha Sahu1
Dr. Gargishankar Verma2
1 M. Tech, Department of Computer Science Engineering, Krishna’s Vikash Institute of Technology, Raipur, Chhattisgarh, India. 2 Professor, Department of CSE, Krishna’s Vikash Institute of Technology, Raipur, Chhattisgarh, India.
Published Online: May-August 2026
Pages: 679-686
Cite this article
No DOIReferences
1. Y. Hasan et al., “Mobile-based CNN framework for detecting papaya diseases, pests, and fruit maturity,” 2026.
2. R. Angrakh, R. Shete, A. Dasgupta, and S. Deshmukh, “Deep learning framework for mango fruit and leaf disease detection using transfer
learning with VGG16 and ResNet50,” 2026.
3. R. Gani et al., “PapayaNet: An attention-guided lightweight CNN for papaya disease classification,” 2025.
4. S. S. Shetty et al., “Papaya disease classification system using YOLOv9c,” 2024.
5. M. Parmar and S. Degadwala, “Papaya disease identification using Vision Transformers (ViTs),” 2024.
6. G. Dwivedi et al., “Automated detection of apple and mango fruit and leaf diseases using MobileNetV2-based Simple Neural Network,”
2024.
7. L. Marlinda, M. Fatchan, W. Widiyawati, F. Aziz, and W. Indrarti, “Mango fruit segmentation using Fuzzy C-Means clustering algorithm,”
2021.
8. M. Hossen et al., “Machine vision-based papaya disease recognition framework using deep learning techniques,” 2021.
9. M. Habib et al., “Online machine vision-based expert system for papaya disease recognition,” 2020.
10. M. Hossen et al., “Deep learning-based papaya disease classification using Convolutional Neural Networks implemented through Keras
API,” 2020.
2. R. Angrakh, R. Shete, A. Dasgupta, and S. Deshmukh, “Deep learning framework for mango fruit and leaf disease detection using transfer
learning with VGG16 and ResNet50,” 2026.
3. R. Gani et al., “PapayaNet: An attention-guided lightweight CNN for papaya disease classification,” 2025.
4. S. S. Shetty et al., “Papaya disease classification system using YOLOv9c,” 2024.
5. M. Parmar and S. Degadwala, “Papaya disease identification using Vision Transformers (ViTs),” 2024.
6. G. Dwivedi et al., “Automated detection of apple and mango fruit and leaf diseases using MobileNetV2-based Simple Neural Network,”
2024.
7. L. Marlinda, M. Fatchan, W. Widiyawati, F. Aziz, and W. Indrarti, “Mango fruit segmentation using Fuzzy C-Means clustering algorithm,”
2021.
8. M. Hossen et al., “Machine vision-based papaya disease recognition framework using deep learning techniques,” 2021.
9. M. Habib et al., “Online machine vision-based expert system for papaya disease recognition,” 2020.
10. M. Hossen et al., “Deep learning-based papaya disease classification using Convolutional Neural Networks implemented through Keras
API,” 2020.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026
