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Rice Leaf Disease Detection using Convolutional Neural Network

Aashi Deshmukh*, Aarya Janghel**, Anuoshka Singh***, Amar Kumar Dey****

*-**** Department of Electronics and Telecommunications Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India.

Periodicity:July - September'2024

Abstract

Rice is one of the most widely cultivated crops in India, playing a crucial role in the country's agricultural economy. However, rice production is frequently threatened by diseases, such as Bacterial Leaf Blight, Brown Spots, and Sheath Blight, which can significantly reduce yield. This study focuses on developing a deep learning based rice leaf disease detection system using Convolutional Neural Networks (CNN). A dataset containing images of rice leaves, categorized into different disease types and healthy leaves, was used to train the model. By applying advanced image processing and deep learning techniques, the system can accurately identify and classify diseases. A user-friendly web application allows farmers to upload images and receive real-time diagnostic feedback, empowering them to implement timely corrective measures. This system offers a scalable and cost-effective solution for enhancing crop management, supporting food security, and promoting sustainable agriculture in India.

Keywords

Rice Leaf Diseases, Bacterial Leaf Blight, Brown Spot, Sheath Blight, Convolutional Neural Network, Deep Learning, Image Processing.

How to Cite this Article?

Deshmukh, A., Janghel, A., Singh, A., and Dey, A. K. (2024). Rice Leaf Disease Detection using Convolutional Neural Network. i-manager’s Journal on Image Processing, 11(3), 10-25.

References

[1]. Ahmed, K., Shahidi, T. R., Alam, S. M. I., & Momen, S. (2019, December). Rice leaf disease detection using machine learning techniques. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1-5). IEEE.

[2]. Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2019). Solving current limitations of deep learning based approaches for plant disease detection. Symmetry, 11(7), 939.

[3]. Benezeth, Y., Jodoin, P. M., Emile, B., Laurent, H., & Rosenberger, C. (2010). Comparative study of background subtraction algorithms. Journal of Electronic Imaging, 19(3), 033003-033003.

[4]. Bhanumathi, V., & Kalaivanan, K. (2019). The role of geospatial technology with IoT for precision agriculture. Cloud Computing for Geospatial Big Data Analytics: Intelligent Edge, Fog and Mist Computing (pp. 225-250).

[5]. Chowdhury, M. E., Rahman, T., Khandakar, A., Ayari, M. A., Khan, A. U., Khan, M. S., & Ali, S. H. M. (2021). Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering, 3(2), 294-312.

[6]. Gupta, A., & Gaur, H. S. (2024). An update on epidemiology of sheath blight pathogen of rice and its management. In Fungal Diseases of Rice and Their Management (pp. 125-153). Apple Academic Press.

[7]. Hassan, S. M., Maji, A. K., Jasinski, M., Leonowicz, Z., & Jasinska, E. (2021). Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics, 10(12), 1388.

[8]. Islam, M. A., Shuvo, M. N. R., Shamsojjaman, M., Hasan, S., Hossain, M. S., & Khatun, T. (2021). An automated convolutional neural network based approach for paddy leaf disease detection. International Journal of Advanced Computer Science and Applications, 12(1), 280-288.

[9]. Jafar, A., Bibi, N., Naqvi, R. A., Sadeghi-Niaraki, A., & Jeong, D. (2024). Revolutionizing agriculture with artificial intelligence: Plant disease detection methods, applications, and their limitations. Frontiers in Plant Science, 15, 1356260.

[10]. Kaur, A., Guleria, K., & Trivedi, N. K. (2021, October). Rice leaf disease detection: A review. In 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 418-422). IEEE.

[11]. Latif, G., Abdelhamid, S. E., Mallouhy, R. E., Alghazo, J., & Kazimi, Z. A. (2022). Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model. Plants, 11(17), 2230.

[12]. Matin, M. M. H., Khatun, A., Moazzam, M. G., & Uddin, M. S. (2020). An efficient disease detection technique of rice leaf using AlexNet. Journal of Computer and Communications, 8(12), 49-57.

[13]. Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., & Ramon, H. (2005). Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging, 11(2), 75-83.

[14]. Naqvi, S. A. H. (2019). Bacterial leaf blight of rice: An overview of epidemiology and management with special reference to Indian sub-continent. Pakistan Journal of Agricultural Research, 32(2), 359-360.

[15]. Pallathadka, H., Ravipati, P., Sajja, G. S., Phasinam, K., Kassanuk, T., Sanchez, D. T., & Prabhu, P. (2022). Application of machine learning techniques in rice leaf disease detection. Materials Today: Proceedings, 51, 2277-2280.

[16]. Paramesh, V., Kumar, P., Parajuli, R., Francaviglia, R., Manohara, K. K., Arunachalam, V., & Toraskar, S. (2023). A life cycle assessment of rice–rice and rice–cowpea cropping systems in the west coast of India. Land, 12(2), 502.

[17]. Pothen, M. E., & Pai, M. L. (2020, March). Detection of rice leaf diseases using image processing. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 424-430). IEEE.

[18]. Priya, R. S., Yuvaraj, M., Sharmila, R., Jagathjothi, N., Saranya, M., Suganthi, N., & Sivaji, M. (2024). Effects of climate change on plant diseases. In Plant Quarantine Challenges under Climate Change Anxiety (pp. 183-225). Cham: Springer Nature Switzerland.

[19]. Rahmawati, S., Devita, R., Zain, R. H., Rianti, E., Lubis, N., & Wanto, A. (2021, June). Prewitt and canny methods on inversion image edge detection: An evaluation. In Journal of Physics: Conference Series, 1933(1), 012039. IOP Publishing.

[20]. Ramesh, S., & Vydeki, D. (2018, September). Rice blast disease detection and classification using machine learning algorithm. In 2018 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE) (pp. 255-259). IEEE.

[21]. Ramesh, S., & Vydeki, D. (2020). Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Information Processing in Agriculture, 7(2), 249-260.

[22]. Saharan, M., & Singh, G. (2023, November). Check for updates leaf disease detection using transfer learning. In Artificial Intelligence: Towards Sustainable Intelligence: First International Conference, AI4S 2023, Pune, India, September 4-5, 2023, Proceedings (pp. 44-58). Springer Nature.

[23]. Sarkar, C., Gupta, D., Gupta, U., & Hazarika, B. B. (2023). Leaf disease detection using machine learning and deep learning: Review and challenges. Applied Soft Computing, 145, 110534.

[24]. Tejaswini, P., Singh, P., Ramchandani, M., Rathore, Y. K., & Janghel, R. R. (2022, June). Rice leaf disease classification using CNN. In IOP Conference Series: Earth and Environmental Science, 1032(1), 012017. IOP Publishing.

[25]. Thanapol, P., Lavangnananda, K., Bouvry, P., Pinel, F., & Leprévost, F. (2020, October). Reducing overfitting and improving generalization in training convolutional neural network (CNN) under limited sample sizes in image recognition. In 2020-5th International Conference on Information Technology (InCIT) (pp. 300-305). IEEE.

[26]. Tugrul, B., Elfatimi, E., & Eryigit, R. (2022). Convolutional neural networks in detection of plant leaf diseases: A review. Agriculture, 12(8), 1192.

[27]. Valarmathi, P., & Ladhalakshmi, D. (2018). Morphological characterization of Bipolaris oryzae causing brown spot disease of rice. International Journal of Current Microbiology and Applied Sciences, 7(2), 161-170.

[28]. Vasantha, S. V., Kiranmai, B., & Krishna, S. R. (2021). Techniques for rice leaf disease detection using machine learning algorithms. International Journal of Engineering Research & Technology, 9(8), 162-166.

[29]. Yu, W., Yang, K., Yao, H., Sun, X., & Xu, P. (2017). Exploiting the complementary strengths of multi-layer CNN features for image retrieval. Neurocomputing, 237, 235-241.

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