Authors : S. Kokila; Dr. S. Abirami; Dr. D. Nagaraju

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/2hk8ajwd

Scribd : https://tinyurl.com/4skumucc

DOI : https://doi.org/10.38124/ijisrt/25sep1543

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Rice is a staple food crop and a vital contributor to global food security. However, rice plants are highly susceptible to multiple diseases, including bacterial leaf blight, brown spot, blast, scald, and narrow brown spot, which can drastically reduce yield and quality. Early and accurate detection of these diseases is critical for precision agriculture. In this study, we propose an integrated framework combining entropy-based image segmentation with a lightweight convolutional neural network (MobileNetV2) for multi-class rice leaf disease detection. Entropy-based masks are generated to highlight diseased regions, and the original RGB image is concatenated with the mask to form a 4-channel input for training. The model is trained on a dataset of six rice leaf classes and evaluated using accuracy, precision, recall, and F1-score. The proposed framework achieved an overall accuracy of 91% on the test set, with high per-class performance (F1-scores: bacterial leaf blight 0.99, leaf scald 0.98, healthy 0.94). Furthermore, the model was exported to TensorFlow Lite (TFLite), demonstrating deployment potential on mobile and IoT devices. These results indicate that integrating segmentation and lightweight CNNs provides a scalable and accurate solution for real-time plant disease detection.

Keywords : Rice Diseases, MobileNetV2, Entropy Segmentation, Precision Agriculture, Deep Learning, Plant Pathology.

References :

1. M Karunya, B. Ravikumar “ Multi-Class CNN-Based Detection of Rice Leaf Diseases Using Cross-Entropy Loss Optimization”  American Journal of AI Cyber Computing Management E-ISSN:3069-0102 VOL.5, NO. 3(2025).
2. Abasi, A.K., Makhadmeh, S.N., Alomari, O.A., Tubishat, M., Mohammed, H.J., 2023.Enhancing rice leaf disease classification: a customized convolutional neural network approach. Sustainability 15 (20), 15039.
3. Bhuyan, P., Singh, P.K., 2024. Evaluating deep CNNs and vision transformers for plant leaf disease classification. In: International Conference on Distributed Computing and Intelligent Technology, pp. 293–306.
4. Firdaus, Mohamad, et al. "Optimizing rice leaf disease classification through convolutional neural network architectural modification and augmentation techniques." International Journal of Electrical & Computer Engineering (2088-8708) 15.3 (2025).
5. Saleem, Muhammad Hammad, Johan Potgieter, and Khalid Mahmood Arif. "Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers." Plants 9, no. 10, pp. 1319, 2020.
6. Archana, R., Jeevaraj, P.E., 2024. Deep learning models for digital image processing: a review. Artif. Intell. Rev. 57 (1), 11.
7. Ishengoma, F.S., Rai, I.A., Ngoga, S.R., 2022. Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using UAVbased images. Eco. Inform. 67, 101502.
8. Tiwari, V., Joshi, R.C., Dutta, M.K., 2021. Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Eco. Inform.63, 101289.
9. P. Krishna Chaitanya , K. Yasudha “ Image based Plant Disease Detection using Convolution Neural Networks Algorithm” International Journal of Innovative Science and Research Technology ISSN No:-2456-2165.  Volume 5, Issue 5, May – 2020.
10. Abhishek S, Divya S Lokesh, Niveditha C S., Jagadeesh Basavaiah, Audre Arlene Anthony “Review of Deep Learning for Plant Disease Detection” International Journal of Innovative Science and Research Technology ISSN No:-2456-2165. Volume 7, Issue 6, June – 2022.
11. N Bhavana; P Likithasree (2025) Plant Disease Detection. International Journal of Innovative Science and Research Technology, 10(4), 3249-3252. https://doi.org/10.38124/ijisrt/25apr2394.
12. Kokila, M., Abirami, D., & Nagaraju, D. (2025). Fast Mask Region And Entropy Based Histogram Equalized Segmentation Of Rice Plant Diseases. Journal of Theoretical and Applied Information Technology, 103(4).
13. Kokila, M., Abirami, D., & Nagaraju, D. (2025). Deep Transfer Learning with MobileNetV2 for Automated Classification of Healthy and Diseased Rice Plant Leaves", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, pp.d:493-d498.
14. https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset?resource=download