Transfer Learning-Based Convolutional Neural Network for Accurate Detection of Rice Leaf Disease in Precision Agriculture
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Background: Traditional approaches to rice disease identification depend mainly upon visual examination, which is quite labor-intensive and generally demands a certain skill level from people engaged in this activity. However, these approaches suffer from high time costs and potential errors and are impractical for large-scale daily monitoring. The recent rise of deep learning has offered opportunities for automated detection process improvement, which needs to be fast-accurate as good farmer-centric.
Objective: This study aims to enhance the accuracy of image rice leaf disease classification via feature extraction for rice leaf disease in four instances of pre-trained CNN models and provide an automated solution for early detection ahead of timely care by obtaining insights into crop production through precision agriculture.
Methods: This study combined transfer learning with four pre-trained CNN models - InceptionResNetV2, MobileNetV2, DenseNet121, and VGG16.
Results: The outcome of this research enables the identification of the optimal model to relate datasets where DenseNet121 achieved the highest accuracy, i.e. 99.10%, followed by MobileNetV2, having a precision of 97.10%.
Conclusion: The new framework results in a highly accurate and high-throughput early disease detection element in precision agriculture, better than state-of-the-art approaches based on traditional techniques.
Keywords: Deep Learning, DenseNet121, Image Classification, Rice Leaf Diseases, Transfer Learning
M. R. Jamal, P. Kristiansen, M. J. Kabir, and L. Lobry de Bruyn, “Challenges and Adaptations for Resilient Rice Production under Changing Environments in Bangladesh,” Land, vol. 12, no. 6, p. 1217, Jun. 2023, doi: 10.3390/land12061217.
M. Huang, “The decreasing area of hybrid rice production in China: causes and potential effects on Chinese rice self-sufficiency,” Food Secur., vol. 14, no. 1, pp. 267–272, Feb. 2022, doi: 10.1007/s12571-021-01199-z.
H. Pallathadka et al., “Application of machine learning techniques in rice leaf disease detection,” in Materials Today: Proceedings, 2022. doi: 10.1016/j.matpr.2021.11.398.
S. Singh, S. Chand, N. K. Singh, and T. R. Sharma, “Genome-Wide Distribution, Organisation and Functional Characterization of Disease Resistance and Defence Response Genes across Rice Species,” PLoS One, vol. 10, no. 4, p. e0125964, Apr. 2015, doi: 10.1371/journal.pone.0125964.
C. Ma et al., “Nanomaterials in agricultural soils: Ecotoxicity and application,” Curr. Opin. Environ. Sci. Heal., vol. 31, p. 100432, Feb. 2023, doi: 10.1016/j.coesh.2022.100432.
G. Agrios, “Plant Pathology 5th Edition,” San Diego Acad. Press, 2005.
H. Bunawan, L. Dusik, S. N. Bunawan, and N. M. Amin, “Rice tungro disease: From identification to disease control,” World Appl. Sci. J., vol. 31, no. 6, 2014, doi: 10.5829/idosi.wasj.2014.31.06.610.
J. Shim et al., “Rice tungro spherical virus resistance into photoperiod-insensitive japonica rice by marker-assisted selection,” 2015. doi: 10.1270/jsbbs.65.345.
V. de P. Bigirimana, G. K. H. Hua, O. I. Nyamangyoku, and M. Hòfte, “Rice sheath rot: An emerging ubiquitous destructive disease complex,” 2015. doi: 10.3389/fpls.2015.01066.
E. Ibrahim, N. Rosida, Khaerana, and M. Haiqal, “Growth and result performance of 10 promising resistant lines of tungro disease in Lanrang South Sulawesi,” IOP Conf. Ser. Earth Environ. Sci., vol. 1230, no. 1, p. 012132, Sep. 2023, doi: 10.1088/1755-1315/1230/1/012132.
Y. M. Shabana, G. M. Abdel-Fattah, A. E. Ismail, and Y. M. Rashad, “Control of brown spot pathogen of rice (Bipolaris oryzae) using some phenolic antioxidants,” Brazilian J. Microbiol., vol. 39, no. 3, 2008, doi: 10.1590/S1517-83822008000300006.
R. Chhabra, R. Sharma, M. S. Hunjan, V. K. Sharma, P. Sharma, and S. K. Chauhan, “Microstructural and metabolic variations induced by Bipolaris oryzae inciting brown spot disease of rice,” Cereal Res. Commun., vol. 51, no. 4, 2023, doi: 10.1007/s42976-023-00351-z.
N. K. Chakrabarti, “Epidemiology and Disease Management of Brown Spot of Rice in India,” in Major Fungal Diseases of Rice, 2001. doi: 10.1007/978-94-017-2157-8_21.
T. R. Sharma, A. K. Rai, S. K. Gupta, J. Vijayan, B. N. Devanna, and S. Ray, “Rice Blast Management Through Host-Plant Resistance: Retrospect and Prospects,” 2012. doi: 10.1007/s40003-011-0003-5.
M. K. Barnwal et al., “A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps,” 2013. doi: 10.1007/s10658-013-0195-6.
M. U. Younas et al., “Approaches to Reduce Rice Blast Disease Using Knowledge from Host Resistance and Pathogen Pathogenicity,” 2023. doi: 10.3390/ijms24054985.
L. Nalley, F. Tsiboe, A. Durand-Morat, A. Shew, and G. Thoma, “Economic and environmental impact of rice blast pathogen (Magnaporthe oryzae) alleviation in the United States,” PLoS One, vol. 11, no. 12, 2016, doi: 10.1371/journal.pone.0167295.
H. L. Chen, B. T. Chen, D. P. Zhang, Y. F. Xie, and Q. Zhang, “Pathotypes of Pyricularia grisea in rice fields of central and southern China,” Plant Dis., vol. 85, no. 8, 2001, doi: 10.1094/PDIS.2001.85.8.843.
J. M. Bonman, “Leaf and Neck Blast Resistance in Tropical Lowland Rice Cultivars,” Plant Dis., vol. 73, no. 5, 1989, doi: 10.1094/pd-73-0388.
P. K. Mannepalli, A. Pathre, G. Chhabra, P. A. Ujjainkar, and S. Wanjari, “Diagnosis of bacterial leaf blight, leaf smut, and brown spot in rice leafs using VGG16,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 193–200. doi: 10.1016/j.procs.2024.04.022.
C. G. Simhadri, H. K. Kondaveeti, V. K. Vatsavayi, A. Mitra, and P. Ananthachari, “Deep learning for rice leaf disease detection: A systematic literature review on emerging trends, methodologies and techniques,” Inf. Process. Agric., vol. 12, no. 2, pp. 151–168, Jun. 2025, doi: 10.1016/j.inpa.2024.04.006.
H. Pallathadka et al., “Application of machine learning techniques in rice leaf disease detection,” Mater. Today Proc., vol. 51, pp. 2277–2280, 2022, doi: 10.1016/j.matpr.2021.11.398.
T. Kavzoglu and A. Teke, “Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost),” Bull. Eng. Geol. Environ., vol. 81, no. 5, p. 201, May 2022, doi: 10.1007/s10064-022-02708-w.
H. Haapala, L. Pesonen, and P. Nurkka, “Usability as a Challenge in Precision Agriculture – Case Study: an ISOBUS VT,” Agric. Eng. Int. CIGR e-journal, vol. VIII, 2006.
A. Nanavaty et al., “Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust,” Sci. Rep., vol. 14, no. 1, p. 28203, Nov. 2024, doi: 10.1038/s41598-024-79793-2.
R. Ahmed, “Leveraging Convolutional Neural Network and Transfer Learning for Cotton Plant and Leaf Disease Recognition,” Int. J. Image, Graph. Signal Process., vol. 13, no. 4, pp. 47–62, 2021, doi: 10.5815/ijigsp.2021.04.04.
A. W. Salehi et al., “A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope,” Sustainability, vol. 15, no. 7, p. 5930, Mar. 2023, doi: 10.3390/su15075930.
S. Asif, W. Yi, Q. U. Ain, J. Hou, T. Yi, and J. Si, “Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images,” IEEE Access, vol. 10, pp. 34716–34730, 2022, doi: 10.1109/ACCESS.2022.3153306.
S. M. Hassan, A. K. Maji, M. Jasiński, Z. Leonowicz, and E. Jasińska, “Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach,” Electronics, vol. 10, no. 12, p. 1388, Jun. 2021, doi: 10.3390/electronics10121388.
K. N, L. V. Narasimha Prasad, C. S. Pavan Kumar, B. Subedi, H. B. Abraha, and V. E. Sathishkumar, “Rice leaf diseases prediction using deep neural networks with transfer learning,” Environ. Res., vol. 198, 2021, doi: 10.1016/j.envres.2021.111275.
R. Dogra, S. Rani, A. Singh, M. A. Albahar, A. E. Barrera, and A. Alkhayyat, “Deep learning model for detection of brown spot rice leaf disease with smart agriculture,” Comput. Electr. Eng., vol. 109, 2023, doi: 10.1016/j.compeleceng.2023.108659.
V. K. Shrivastava and M. K. Pradhan, “Rice plant disease classification using color features: a machine learning paradigm,” J. Plant Pathol., vol. 103, no. 1, 2021, doi: 10.1007/s42161-020-00683-3.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, 2022, doi: 10.1016/j.gltp.2022.04.020.
K. Alomar, H. I. Aysel, and X. Cai, “Data Augmentation in Classification and Segmentation: A Survey and New Strategies,” J. Imaging, vol. 9, no. 2, p. 46, Feb. 2023, doi: 10.3390/jimaging9020046.
B. Neupane, T. Horanont, and J. Aryal, “Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network,” Sensors, vol. 22, no. 10, p. 3813, May 2022, doi: 10.3390/s22103813.
S. Faisal et al., “Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases,” Comput. Mater. Contin., vol. 76, no. 1, pp. 895–914, 2023, doi: 10.32604/cmc.2023.039781.
A. Arya and P. K. Mishra, “MobileNetV2-Incep-M: a hybrid lightweight model for the classification of rice plant diseases,” Multimed. Tools Appl., vol. 83, no. 33, pp. 79117–79144, Mar. 2024, doi: 10.1007/s11042-024-18723-w.
V. Sahasranamam, T. Ramesh, R. Rajeswari, A. Karthikkumar, and D. Muthumanickam, “AI-Driven Paddy Leaf Disease Classification and Prediction using DenseNet-121,” 2025, pp. 272–292. doi: 10.2991/978-94-6463-740-3_24.
J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, p. 105393, Jun. 2020, doi: 10.1016/j.compag.2020.105393.
B. S. Bari et al., “A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework,” PeerJ Comput. Sci., vol. 7, 2021, doi: 10.7717/PEERJ-CS.432.
N. Nandhini and R. Bhavani, “Feature extraction for diseased leaf image classification using machine learning,” in 2020 International Conference on Computer Communication and Informatics, ICCCI 2020, 2020. doi: 10.1109/ICCCI48352.2020.9104203.
“Rice Leafs Disease Dataset,” https://www.kaggle.com/datasets/maimunulkjisan/rice-leaf-dataset-from-mendeley-data. Accessed: Aug. 31, 2024. [Online]. Available: https://www.kaggle.com/datasets/maimunulkjisan/rice-leaf-dataset-from-mendeley-data
R. S. Tiwari, L. Dandabani, T. K. Das, S. B. Khan, S. Basheer, and M. S. Alqahtani, “Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants,” Diagnostics, vol. 13, no. 22, 2023, doi: 10.3390/diagnostics13223419.
F. Pedregosa FABIANPEDREGOSA et al., “Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot,” 2011. [Online]. Available: http://scikit-learn.sourceforge.net.
M. M. Rahman et al., “Explainable Deep Transfer Learning Framework for Rice Leaf Disease Diagnosis and Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 12, pp. 862–884, 2024, doi: 10.14569/IJACSA.2024.0151287
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