Image-Based Deep Learning Method for Detecting Diseases in Rice Plants
Received: 5 May 2025 | Revised: 3 June 2025, 1 July 2025, 21 July 2025, 7 August 2025, and 29 August 2025 | Accepted: 9 September 2025 | Online: 9 October 2025
Corresponding author: S. Kokila
Abstract
India's rapidly growing population demands increased agricultural productivity, particularly in rice cultivation, which is vital to national food security. However, rice crops are highly susceptible to various leaf diseases such as Brown Spot, Blast, and Bacterial Blight, which significantly reduce yield and quality. Timely and accurate identification of these diseases is essential to prevent widespread outbreaks and reduce excessive pesticide use. This study proposes a deep learning-based approach for automated rice leaf disease detection. Three state-of-the-art models—YOLOv5, DenseNet-201, and ResNet-101—were evaluated using a publicly available rice disease dataset. All models were trained and tested on the Google Colab platform. This study aimed to identify the most effective and practical model for real-world deployment. The results show that YOLOv5 outperformed DenseNet-201 and ResNet-101 in detection accuracy, offering a robust and scalable solution for real-time disease identification. This work contributes to precision agriculture by enabling early diagnosis and informed decision-making, ultimately improving crop health and promoting sustainable farming practices.
Keywords:
deep learning, DenseNet, ResNet, YOLOv5, rice leaf diseaseDownloads
References
P. I. Ritharson, K. Raimond, X. A. Mary, J. E. Robert, and A. J, "DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes," Artificial Intelligence in Agriculture, vol. 11, pp. 34–49, Mar. 2024. DOI: https://doi.org/10.1016/j.aiia.2023.11.001
A. D. J. Van Dijk, G. Kootstra, W. Kruijer, and D. De Ridder, "Machine learning in plant science and plant breeding," iScience, vol. 24, no. 1, Jan. 2021, Art. no. 101890. DOI: https://doi.org/10.1016/j.isci.2020.101890
F. Jiang, Y. Lu, Y. Chen, D. Cai, and G. Li, "Image recognition of four rice leaf diseases based on deep learning and support vector machine," Computers and Electronics in Agriculture, vol. 179, Dec. 2020, Art. no. 105824. DOI: https://doi.org/10.1016/j.compag.2020.105824
H. M. Yusuf, S. A. Yusuf, A. H. Abubakar, M. Abdullahi, and I. H. Hassan, "A systematic review of deep learning techniques for rice disease recognition: Current trends and future directions," Franklin Open, vol. 8, Sep. 2024, Art. no. 100154. DOI: https://doi.org/10.1016/j.fraope.2024.100154
K. Ahmed, T. R. Shahidi, S. Md. Irfanul Alam, and S. Momen, "Rice Leaf Disease Detection Using Machine Learning Techniques," in 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, Dec. 2019, pp. 1–5. DOI: https://doi.org/10.1109/STI47673.2019.9068096
B. S. Anami, N. N. Malvade, and S. Palaiah, "Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images," Artificial Intelligence in Agriculture, vol. 4, pp. 12–20, 2020. DOI: https://doi.org/10.1016/j.aiia.2020.03.001
W. Liang, H. Zhang, G. Zhang, and H. Cao, "Rice Blast Disease Recognition Using a Deep Convolutional Neural Network," Scientific Reports, vol. 9, no. 1, Feb. 2019, Art. no. 2869. DOI: https://doi.org/10.1038/s41598-019-38966-0
K. Li, X. Li, B. Liu, C. Ge, Y. Zhang, and L. Chen, "Diagnosis and application of rice diseases based on deep learning," PeerJ Computer Science, vol. 9, Jun. 2023, Art. no. e1384. DOI: https://doi.org/10.7717/peerj-cs.1384
K. A. Vakilian and J. Massah, "Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks," Archives Of Phytopathology And Plant Protection, vol. 46, no. 11, pp. 1262–1269, Jul. 2013. DOI: https://doi.org/10.1080/03235408.2013.763620
R. Mishra, R. K. Joshi, and K. Zhao, "Genome Editing in Rice: Recent Advances, Challenges, and Future Implications," Frontiers in Plant Science, vol. 9, Sep. 2018, Art. no. 1361. DOI: https://doi.org/10.3389/fpls.2018.01361
Y. Tang, C. Chen, A. C. Leite, and Y. Xiong, "Editorial: Precision control technology and application in agricultural pest and disease control," Frontiers in Plant Science, vol. 14, Feb. 2023, Art. no. 1163839. DOI: https://doi.org/10.3389/fpls.2023.1163839
T. Daniya and D. S. Vigneshwari, "A Review on Machine Learning Techniques for Rice Plant Disease Detection in Agricultural Research," International Journal of Advanced Science and Technology, vol. 28, no. 13, pp. 49–62, 2019.
J. P. Shah, H. B. Prajapati, and V. K. Dabhi, "A survey on detection and classification of rice plant diseases," in 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Bangalore, India, Mar. 2016, pp. 1–8. DOI: https://doi.org/10.1109/ICCTAC.2016.7567333
A. A. Joshi and B. D. Jadhav, "Monitoring and controlling rice diseases using Image processing techniques," in 2016 International Conference on Computing, Analytics and Security Trends (CAST), Pune, India, Dec. 2016, pp. 471–476. DOI: https://doi.org/10.1109/CAST.2016.7915015
A. Stephen, A. Punitha, and A. Chandrasekar, "Designing self attention-based ResNet architecture for rice leaf disease classification," Neural Computing and Applications, vol. 35, no. 9, pp. 6737–6751, Mar. 2023. DOI: https://doi.org/10.1007/s00521-022-07793-2
X. Zhang, Y. Qiao, F. Meng, C. Fan, and M. Zhang, "Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks," IEEE Access, vol. 6, pp. 30370–30377, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2844405
Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, "Identification of rice diseases using deep convolutional neural networks," Neurocomputing, vol. 267, pp. 378–384, Dec. 2017. DOI: https://doi.org/10.1016/j.neucom.2017.06.023
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, Aug. 2020, Art. no. 105527. DOI: https://doi.org/10.1016/j.compag.2020.105527
S. S. Chouhan, U. P. Singh, U. Sharma, and S. Jain, "Leaf disease segmentation and classification of Jatropha Curcas L. and Pongamia Pinnata L. biofuel plants using computer vision based approaches," Measurement, vol. 171, Feb. 2021, Art. no. 108796. DOI: https://doi.org/10.1016/j.measurement.2020.108796
D. Tandekar and S. Dongre, "Identification of Various Diseases in Plant Leaves Using Image Processing and CNN Approach," in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, Jul. 2023, pp. 1–6. DOI: https://doi.org/10.1109/ICCCNT56998.2023.10306979
W. L. Chen, Y. B. Lin, F. L. Ng, C. Y. Liu, and Y. W. Lin, "RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies," IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1001–1010, Feb. 2020. DOI: https://doi.org/10.1109/JIOT.2019.2947624
N. Jiang et al., "Resistance Genes and their Interactions with Bacterial Blight/Leaf Streak Pathogens (Xanthomonas oryzae) in Rice (Oryza sativa L.)—an Updated Review," Rice, vol. 13, no. 1, Dec. 2020, Art. no. 3. DOI: https://doi.org/10.1186/s12284-019-0358-y
S. Dariush, M. Darvishnia, A. A. Ebadi, F. Padasht-Dehkaei, and E. Bazgir, "Screening rice genotypes for brown spot resistance along with yield attributing characters and its association with morphological traits," Journal of Crop Protection, vol. 9, no. 3, pp. 381–393, Sep. 2020.
S. R. Shah, S. Qadri, H. Bibi, S. M. W. Shah, M. I. Sharif, and F. Marinello, "Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease," Agronomy, vol. 13, no. 6, Jun. 2023, Art. no. 1633. DOI: https://doi.org/10.3390/agronomy13061633
Y. Li, X. Chen, L. Yin, and Y. Hu, "Deep Learning-Based Methods for Multi-Class Rice Disease Detection Using Plant Images," Agronomy, vol. 14, no. 9, Aug. 2024, Art. no. 1879. DOI: https://doi.org/10.3390/agronomy14091879
Z. Chen et al., "Plant Disease Recognition Model Based on Improved YOLOv5," Agronomy, vol. 12, no. 2, Jan. 2022, Art. no. 365. DOI: https://doi.org/10.3390/agronomy12020365
E. Iren, "Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13714–13719, Apr. 2024. DOI: https://doi.org/10.48084/etasr.7033
"Rice Leafs Disease Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/dedeikhsandwisaputra/rice-leafs-disease-dataset.
Downloads
How to Cite
License
Copyright (c) 2025 S. Kokila, S. Abirami, D. Nagaraju
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
