Real-time Identification of Rice Leaf Diseases using Convolutional Neural Networks
DOI:
https://doi.org/10.47839/ijc.23.4.3773Keywords:
rice disease, CNN, YOLO, Faster R-CNNAbstract
Rice is one of the most important crops worldwide, serving as a primary food source for millions of people. However, this crop is threatened by diseases such as Rice Blast, Brown Spot, and Bacterial Blight, which manifest in the leaves of the plant. The characteristics of these diseases, captured in digital images, can be utilized in computer vision techniques for their detection and classification. In this study, two Convolutional Neural Networks, YOLO version 8 and Faster R-CNN, were compared to detect and classify the diseases Blast and Brown Spot in a dataset comprising 3636 images with 7915 annotations indicating the location of the disease on the rice leaves. The model trained using YOLO version 8 achieved an accuracy of 92.98% and a recall of 92.45%, while the model trained with Faster R-CNN achieved an accuracy of 91.99% and a recall of 87.78%. YOLO has lower inference times compared to Faster R-CNN due to its more efficient approach and simpler architecture.
References
Z. Jiang, Z. Dong, W. Jiang, and Y. Yang, “Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning,” Comput. Electron. Agric., vol. 186, p. 106184, 2021, https://doi.org/10.1016/j.compag.2021.106184.
V. Shanmugam, T. V. Madhusudhana Rao, H. J. Rao, and B. Maram, “Internet of things based smart application for rice leaf disease classification using optimization integrated deep maxout network,” Concurrency and Computation: Practice and Experience, vol. 35, no. 6, p. 1, 2023, https://doi.org/10.1002/cpe.7545.
AgroPerú, “Enfermedades que ponen en riesgo la producción de arroz,” AGROPERÚ Informa. [Online]. Available at: https://www.agroperu.pe/enfermedades-que-ponen-en-riesgo-la-produccion-de-arroz/
F. Doni et al., “Leaf blight disease of rice caused by Pantoea: profile of an increasingly damaging disease in rice,” Trop. Plant Pathol., vol. 48, no. 1, pp. 1–10, 2023, https://doi.org/10.1007/s40858-022-00540-x.
R. Sharma et al., “Plant disease diagnosis and image classification using deep learning,” CMC-Comput. Mat. Contin., vol. 71, no. 2, pp. 2125–2140, 2022, https://doi.org/10.32604/cmc.2022.020017.
M. Karthick, D. Vijayalakshmi, M. K. Nath, and M. Mathumathi, “Machine learning approaches for automatic disease detection from paddy crops – A review,” International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 392–405, 2022, https://doi.org/10.14445/22315381/IJETT-V70I12P237.
A. Nayak, S. Chakraborty, and D. K. Swain, “Application of smartphone-image processing and transfer learning for rice disease and nutrient deficiency detection,” Smart Agricultural Technology, vol. 4, p. 100195, 2023, https://doi.org/10.1016/j.atech.2023.100195.
Y. K. Rathore et al., “Detection of rice plant disease from RGB and grayscale images using an LW17 deep learning model,” Electronic Research Archive, vol. 31, no. 5, pp. 2813–2833, 2023. https://doi.org/10.3934/era.2023142.
D. J. Chaudhari and K. Malathi, “Detection and prediction of rice leaf disease using a hybrid CNN-SVM model,” Optical Memory and Neural Networks (Information Optics), vol. 32, no. 1, pp. 39–57, 2023. https://doi.org/10.3103/S1060992X2301006X.
G. Latif, S. E. Abdelhamid, R. E. Mallouhy, J. Alghazo, and Z. A. Kazimi, “Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN Model,” Plants, vol. 11, no. 17, 2022, https://doi.org/10.3390/plants11172230.
S. Sakhamuri and K. K. Kumar, “Optimal training ensemble of classifiers for classification of rice leaf disease,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, pp. 94–105, 2023, https://doi.org/10.14569/IJACSA.2023.0140311.
K. M. Sudhesh, V. Sowmya, P. Sainamole Kurian, and O. K. Sikha, “AI based rice leaf disease identification enhanced by dynamic mode decomposition,” Engineering Applications of Artificial Intelligence, vol. 120, 105836, 2023, https://doi.org/10.1016/j.engappai.2023.105836.
V. Rajpoot, A. Tiwari, and A. S. Jalal, “Automatic early detection of rice leaf diseases using hybrid deep learning and machine learning methods,” Multimedia Tools and Applications, vol. 82, pp. 36091–36117, 2023, https://doi.org/10.1007/s11042-023-14969-y.
P. Negro and C. Pons, “Artificial Intelligence techniques based on the integration of symbolic logic and deep neural networks: A systematic literature review,” Inteligencia Artificial, vol. 25, no. 69, pp. 13–41, 2022, https://doi.org/10.4114/intartif.vol25iss69pp13-41.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Computational Intelligence and Neuroscience, vol. 2018, p. 7068349, 2018, https://doi.org/10.1155/2018/7068349.
R. Girshick, “Fast R-CNN,” Arxiv, 2015, doi: https://doi.org/10.1109/ICCV.2015.169.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” ArXiv, 2016, https://doi.org/10.1109/TPAMI.2016.2577031.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 2016, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91.
S. Wenkel, K. Alhazmi, T. Liiv, S. Alrshoud, and M. Simon, “Confidence score: The forgotten dimension of object detection performance evaluation,” Sensors, vol. 21, no. 13, 4350, 2021, https://doi.org/10.3390/s21134350.
C. Mota-Delfin, G. de J. López-Canteñs, I. L. López-Cruz, E. Romantchik-Kriuchkova, and J. C. Olguín-Rojas, “Detection and counting of corn plants in the presence of weeds with convolutional neural networks,” Remote Sensing, vol. 14, no. 19, 4892, 2022, https://doi.org/10.3390/rs14194892.
F. Dang, D. Chen, Y. Lu, and Z. Li, “YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems,” Computers and Electronics in Agriculture, vol. 205, 107655, 2023. https://doi.org/10.1016/j.compag.2023.107655.
Md. J. A. Soeb et al., “Tea leaf disease detection and identification based on YOLOv7 (YOLO-T),” Scientific Reports, vol. 13, no. 1, 6078, 2023. https://doi.org/10.1038/s41598-023-33270-4.
D. S. Alwan and M. H. Naji, “Rice diseases detection and classification using you only look once and convolutional neural network,” International Journal on Technical and Physical Problems of Engineering, vol. 15, no. 4. pp. 116–122, 2023.
K. U. Shetty, R. Javed Kutty, K. Donthi, A. Patil, and N. Subramanyam, “Plant disease detection for Guava and Mango using YOLO and faster R-CNN,” Proceedings of the 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI’2024, 2024, pp. 1-6. https://doi.org/10.1109/IATMSI60426.2024.10503209.
I. Kumar, A. Bisht, and J. Rawat, “Identification of tomato plant disease using faster R-CNN and RetinaNet,” Convergence of Cloud Computing, AI, and Agricultural Science, 2023, pp. 306-327. https://doi.org/10.4018/979-8-3693-0200-2.ch016.
B. S. Bari et al., “A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework,” Peer J Computer Science, vol. 7, 2021. https://doi.org/10.7717/peerj-cs.432.
A. E. Asibi, Q. Chai, and J. A. Coulter, “Rice blast: A disease with implications for global food security,” Agronomy, vol. 9, no. 8, 451, 2019, https://doi.org/10.3390/agronomy9080451.
P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Deep feature based rice leaf disease identification using support vector machine,” Computers and Electronics in Agriculture, vol. 175, p. 105527, 2020, https://doi.org/10.1016/j.compag.2020.105527.
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