International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Fruits Disease Detection Using Machine Learning |
|---|---|
| ABSTRACT | Fruit farming is very important for Indian agriculture but many farmers face big losses because of plant diseases. This project makes a smart system that can find diseases in fruits like pomegranate, guava, orange and mango using computer technology. The system uses two different artificial intelligence models - one is simple CNN model and other is DenseNet121 model for checking fruit leaf images. Both models were trained with 20 epochs on dataset having different disease categories. The CNN model has six convolutional layers with max pooling and the DenseNet model uses transfer learning from ImageNet. Images are resized to 224x224 pixels and data augmentation techniques like random flip and rotation are applied. The web application built with Flask framework allows farmers to upload fruit leaf pictures and get instant disease prediction with confidence scores. Prevention methods and yield prediction information is also provided. Testing shows that both models give good accuracy in identifying diseases. This automated system helps farmers detect problems early and take correct actions to protect their crops and reduce economic losses. |
| AUTHOR | DHANUSH D, DR RAGHAVENDRA S P PG Student, Dept. of MCA, Jawaharlal Nehru New College of Engineering, Shivamogga, Karanataka, India Assistant Professor, Dept. of MCA, Jawaharlal Nehru New College of Engineering, Shivamogga, Karanataka, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405096 |
| pdf/96_Fruits Disease Detection Using Machine Learning.pdf | |
| KEYWORDS | |
| References | 1. Attallah, O. Tomato leaf disease classification using compact CNN. Horticulturae, 9(2), 149. 2023. 2. Eunice, J., Popescu, D. E., Chowdary, M. K., & Hemanth, J. Deep learning-based leaf disease detection in crops. Agronomy, 12(12), 2395. 2022. 3. El Fatimi, E. H. Leaf diseases detection using deep learning methods. arXiv Preprint. 2024. 4. Hassan, S. M., Maji, A. K., Jasiński, M., Leonowicz, Z., & Jasińska, E. Identification of plant leaf diseases using CNN and transfer learning. Electronics, 10(12), 1388. 2021. 5. Hosen Foysal, M. A., Ahmed, F., & Haque, M. Z. Multi-class plant leaf disease detection using CNN with mobile integration. arXiv Preprint. 2024. 6. Ishak, P. Vision transformer for maize leaf disease detection. Expert Systems with Applications, 238, 122099. 2024. 7. Javidan, S. M., et al. Grape leaf disease detection using clustering and machine learning. Smart Agricultural Technology, 3, 100081.2023. 8. Jelali, M. M., et al. Deep learning networks-based tomato disease and pest detection: A review using real field datasets. Frontiers in Plant Science, 15, 1493322. 2024. 9. Karim, M. J., Goni, M. O. F., Nahiduzzaman, M., Ahsan, M., & Haider, J. Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM. Scientific Reports, 14, 16022. 2024. 10. Kaur, P., et al.Hybrid CNN for leaf disease recognition. Sensors, 22(2), 575.2022 11. Liu, J., & Wang, X. (2020). Tomato diseases and pests detection based on improved YOLOv3 convolutional neural network. Frontiers in Plant Science, 11, 898. 12. Mustofa, S., Munna, M. M. H., Emon, Y. R., Rabbany, G., & Ahad, M. T. A comprehensive review on plant leaf disease detection using deep learning. arXiv Preprint. 2023. 13. Ouamane, A., et al. Enhancing plant disease detection using tensor subspace learning and CNN models. Applied Soft Computing, 168, 114504. 2024. 14. Peng, Y., & Wang, Y. Leaf disease image retrieval using deep learning. Frontiers in Plant Science, 13, 963302. 2022. 15. Perveen, K., et al. Apple leaf disease detection using attention CNN. Journal of Food Quality, 2023, 9504186. 2023. 16. Perveen, K., et al. Multidimensional attention-based CNN model for apple leaf disease detection. Journal of Food Quality, 2023, 9504186. 2023. 17. Rahman, S. U., Alam, F., Ahmad, N., & Arshad, S. Image processing based system for tomato leaf disease detection. Multimedia Tools and Applications, 82, 9431–9445. 2023. 18. Roy, A. M., & Bhaduri, J. Deep learning enabled multi-class plant disease detection. AI, 2(3), 413–428. 2021. 19. Sanida, M. V.,et al. Hybrid CNN classification model for tomato crop disease. Technologies, 11(1), 10. 2023. 20. Sharma, R. P., Dharavath, R., & Edla, D. R. IoFT-based crop pest detection system. Internet of Things, 21, 100658. 2023. |
