Early-Stage Qualifier and Disease Classification for Pomegranate using Deep Learning
Keywords:
CNN, Deep Learning, MobileNetV2, Random Forest, ResNet50, SVMAbstract
Pomegranate cultivation plays a vital role in India’s agricultural economy. However, diseases such as Bacterial Blight, Alternaria, Anthracnose, and Cercospora severely affect crop yield and fruit quality, leading to significant economic losses for farmers. This paper presents an intelligent, web-based system that automates pomegranate disease detection and stage prediction using a combination of deep learning and machine learning models. The proposed system employs MobileNetV2 and ResNet50 architectures for image-based disease classification and an SVM–Random Forest ensemble for predicting the severity stages (Low, Medium, High) using texture-based GLCM features. A custom dataset of healthy and diseased fruit images was used for training and validation, with preprocessing and augmentation to improve generalization. The web application integrates Flask (backend) and React (frontend) to provide real-time image uploads, camera-based detection, and dynamic multilingual advisories in English, Kannada, Telugu, Tamil, and Hindi. The ensemble model achieved a classification accuracy of 98.4% for disease identification and 84% for stage prediction, demonstrating robust performance and practical applicability. This research contributes toward precision agriculture by offering an accessible, AI-powered advisory tool that supports early detection, reduces crop loss, and empowers farmers with actionable insights in their local languages.
References
H. S. Rana, M. N., S. S. Pokhare, R. A. Marathe , J. Rajan, "Convolutional Neural Network Based Approach for Automatic Detection of Diseases from Pomegranate Plants," 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India, 2024, pp. 66-72.
A. Gupta, S. Mishra, Saweksha, and V. Kukreja, “Automated Detection and Classification of Pomegranate Diseases Using CNN and Random Forest,” 2024 International Conference on Automation and Computation (AUTOCOM), Mar. 2024.
Sahebgouda, M. Sumana, "Disease Detection In Pomegranate Plant Using Image Processing Techniques," 2023 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2023, pp. 1-6.
D. V. Narayana, B. Pakruddin, Y. V. Reddy, A. Chikmurge, R. Mayur, and T. Lokesh, “Performance Analysis of Machine and Deep Learning Techniques for Pomegranate Fruit Disease Detection and Classification,” 2025 International Conference on Next Generation Communication & Information Processing (INCIP), pp. 628–633, Jan. 2025.
S. P. Kumar, “Computer-Based Facial Expression Recognition,” International Journal for Research in Applied Science and Engineering Technology, vol. 8, no. 6, pp. 1204–1207, Jun. 2020.
S. M. Metagar and G. A. Walikar, "Machine Learning Based Pomegranate Plant Leaf Disease Features Extraction and Evaluation," 2024 Second International Conference on Advances in Information Technology (ICAIT), Chikkamagaluru, Karnataka, India, 2024, pp. 1-5.
M. D. Nirmal, P. Jadhav, S. Pawar, and M. Kharde, “Deep Learning-based Disease Detection using Pomegranate Leaf Image,” IEEE Xplore, Dec. 01, 2022.
R. G. Tiwari, A. Misra, H. Maheshwari, A. K. Agarwal and M. Sharma, "Hybrid Transformer-CNN Model for Automated Diagnosis of Pomegranate Fruit Diseases," 2024 10th International Conference on Electrical Energy Systems (ICEES), Chennai, India, 2024, pp. 1-5.
P. B. Wakhare, S. Neduncheliyan and K. R. Thakur, "Study of Disease Identification in Pomegranate Using Leaf Detection Technique," 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2022, pp. 1-6.
R. Pawar and A. Jadhav, "Pomogranite disease detection and classification," 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 2017, pp. 2475-2479.
B. Pakruddin and R. Hemavathy, "Detection of Fusarium Wilt Disease in Pomegranate Plants using Deep Learning Techniques," 2024 International Conference on Modeling, Simulation & Intelligent Computing (MoSICom), Dubai, United Arab Emirates, 2024, pp. 212-217.
R. T. V, K. C. Gouda, and S. S. Kumar, “Novel Approach for Spatiotemporal Weather Data Analysis,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 7, 2022.
P. Wakhare, S. Neduncheliyan, and P. B. Mane, “Machine Learning for Accurate and Efficient Pomegranate Fruit Disease Detection: A Novel Approach to Improve Crop Yield and Quality,” 2023 7th International Conference on Computing, Communication, Control, and Automation (ICCUBEA), pp. 1–5, Aug. 2023.
S. Thakur, D. Malik, V. Kukreja, R. Sharma, R. Yadav, and K. Joshi, “Multi-Stage Classification of Pomegranate Anthracnose Disease Severity Levels with CNN and SVM,” 2023 4th International Conference on Smart Electronics and Communication (ICOSEC), Sep. 2023.
S. Mehta, V. Kukreja, and S. Vats, “Scalable and Privacy-Severity Analysis of Pomegranate Leaf Diseases: Federated Learning with CNNs,” 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Jul. 2023.
