Detection of Rice Plant Diseases Based on Deep Transfer Learning

Year 2023, Volume: 9 Issue: 4, 92 - 99, 31.12.2023

Necaattin Barışçı , Merve Güllü , İbrahim Alper Doğru

Abstract

Rice, obtained through the processing of paddy, is one of the most widely consumed food products globally. However, diseases affecting rice plants, particularly those occurring on the rice leaves, pose significant challenges for farmers. The identification of rice plant diseases demands specialized knowledge, making it a complex issue to tackle. Often, due to insufficient understanding, farmers misdiagnose diseases and apply incorrect treatments. Rapid and accurate disease diagnosis plays a pivotal role in enhancing healthy and productive crop cultivation. To address this problem, a deep learning-based model was developed to detect rice plant diseases. The model was trained on a dataset containing four different rice plant diseases and achieved a successful outcome with a loss value of 0.0014. Additionally, four different deep learning algorithms were used to create models through transfer learning with pre-trained ImageNet models, and a comparison of their performance was presented. The most successful model was obtained using the VGG16 transfer learning architecture. Experimental results in this study demonstrate that the proposed transfer learning method can effectively recognize rice leaf diseases, providing a reliable approach for identifying leaf diseases in various plants.

Keywords

rice plant diseases, deep learning, transfer learning

Project Number

Gazi Üniversitesi BAP Projesi FGA-2022-7973

Derin Transfer Öğrenmeye Dayalı Pirinç Bitkisi Hastalıklarının Tespiti

Abstract

Çeltiğin işlenmesi sonucu elde edilen pirinç, dünyada en çok tüketilen gıda ürünlerinden bir tanesidir. Bitki yapraklarında özellikle, çeltik yapraklarında oluşan hastalıklar çiftçilerin karşılaştığı önemli sorunlardan biridir. Çeltik bitkisi hastalıkları uzman bilgisi gerektirdiğinden dolayı zor bir problemdir. Çiftçiler mahsul hastalıkları hakkında yeterince bilgi sahibi olmadıklarından dolayı hastalık için yanlış tespit yapılmakta ve yanlış tedavi uygulanmaktadır. Hastalıkların hızlı ve doğru olarak tanınması, sağlıklı ve verimli üretimin artmasındaki en önemli süreçtir. Bu tür problemlere çözüm sunmak amacıyla çeltik bitkisi hastalıklarını tespit eden derin öğrenme tabanlı bir model geliştirilmiştir. Dört farklı çeltik bitkisi hastalığı içeren veri kümesi üzerinde model eğitilmiş ve 0,0014 kayıp değeri ile başarılı bir model oluşturulmuştur. Eğitilmiş ImageNet modelleri üzerinde transfer öğrenme metodu ile modeller oluşturmak için dört farklı derin öğrenme algoritması kullanılmış ve bu modellerin performansları karşılaştırılmıştır. En başarılı model VGG16 transfer öğrenme mimarisi ile elde edilmiştir. Deneysel sonuçlar, bu çalışmada önerilen transfer öğrenme yönteminin pirinç yaprağı hastalıklarını tanıyabildiğini ve bunun da birçok bitkinin yaprak hastalıklarını tanımak için güvenilir bir yöntem sağladığını göstermektedir.

Keywords

çeltik bitkisi hastalıkları, derin öğrenme, transfer öğrenme

Supporting Institution

Gazi Üniversitesi BAP Projesi FGA-2022-7973

Project Number

Gazi Üniversitesi BAP Projesi FGA-2022-7973

Thanks

Teşekkür: Bu çalışma FGA-2022-7973 nolu Gazi Üniversitesi BAP Projesi tarafından desteklenmektedir.

References

• [1] M. Shahbandeh, “Grain production worldwide 2022/23, by type,” Sept. 20, 2023. [Online]. Available: https://www.statista.com/statistics/263977/world-grain-production-by-type/. [Accessed: Nov. 10, 2023]
• [2] N. Krishnamoorthy, L.V.N. Prasad, C.S.P. Kumar, B. Subedi, H.B. Abraha and V.E. Sathishkumar, “Rice leaf diseases prediction using deep neural networks with transfer learning,” Environmental Research, Vol. 198, pp. 1-8, 2021. doi: 10.1016/j.envres.2021.111275.
• [3] O. Wallach, “Visualizing the World’s Biggest Rice Producers,” Feb. 23, 2022. [Online]. Available: https://www.visualcapitalist.com/worlds-biggest-rice-producers/. [Accessed: Nov. 10, 2023]
• [4] K. M. Sudhesh, , V. Sowmya, , S. Kurian and O. K. Sikha, “AI based rice leaf disease identification enhanced by Dynamic Mode Decomposition,” Engineering Applications of Artificial Intelligence, Vol. 120, pp. 1-22, 2023. doi: 10.1016/j.engappai.2023.105836.
• [5] 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,” Computers and Electrical Engineering, Vol. 109, pp. 1-11, 2023. doi: 10.1016/j.compeleceng.2023.108659.
• [6] C. Zhou, Y. Zhong, S. Zhou, J. Song and W. Xiang, “Rice leaf disease identification by residual-distilled transformer,” Engineering Applications of Artificial Intelligence. Vol. 121, pp. 1-9, 2023. doi: 10.1016/j.engappai.2023.106020.
• [7] L. Yang, X. Yu, S. Zhang, H. Long, H, Zhang, S. Xu and Y. Liao, “GoogLeNet based on residual network and attention mechanism identification of rice leaf diseases,” Computers and Electronics in Agriculture, Vol. 204, pp. 1-11, 2023. doi: 10.1016/j.compag.2022.107543.
• [8] T, G. Devi and P. Neelamegam, “Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu,” Cluster Computing, Vol. 22, pp. 13415–13428, 2019. doi: 10.1007/s10586-018-1949-x.
• [9] M. Al-Amin, D. Z. Karim and T. A. Bushra, “Prediction of Rice Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System,” in 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, Dhaka, Bangladesh, 18-20 December 2019, pp. 1–5,
• [10] M. E. Pothen and M. L. Pai, "Detection of Rice Leaf Diseases Using Image Processing," in 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 424-430, 2020. doi: 10.1109/ICCMC48092.2020.ICCMC-00080.
• [11] S. Ramesh and D. Vydeki, “Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm,” Information Processing in Agriculture, Vol. 7, 2020, pp. 249-260, doi: 10.1016/j.inpa.2019.09.002.
• [12] S. Ghosal and K. Sarkar, “Rice Leaf Diseases Classification Using CNN With Transfer Learning,” in 2020 IEEE Calcutta Conference, CALCON 2020, Kolkata, India, 28-29 February 2020, pp. 230-236.
• [13] B.S. Bari, M.N. Islam, M.M. Rashid, M.J. Hasan, M.A. Razman, R.M. Musa, A.F. Nasir and A.P. Majeed, “A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework,” PeerJ Computer Science, Vol. 7, pp. 1-27, 2021. doi: 10.7717/peerj-cs.432.
• [14] P. Mekha and N. Teeyasuksaet, "Image Classification of Rice Leaf Diseases Using Random Forest Algorithm," in 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, Cha-am, Thailand, 03-06 March 2021, pp. 165-169,
• [15] S. M. Shahidur Harun Rumy, M. I. Arefin Hossain, F. Jahan and T. Tanvin, "An IoT based System with Edge Intelligence for Rice Leaf Disease Detection using Machine Learning," in 2021 IEEE International IOT, Electronics and Mechatronics Conference IEMTRONICS 2021, Toronto, ON, Canada, 21-24 April 2021, pp. 1-6,
• [16] S.K. Upadhyay and A. Kumar, “A novel approach for rice plant diseases classification with deep convolutional neural network” Internetional Journal of Information Technology, Vol. 14, pp. 185–199, 2022. doi: 10.1007/s41870-021-00817-5. [17] 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, pp. 1-9, 2020. doi: 10.1016/j.compag.2020.105527.
• [18] 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,” Computers and Electronics in Agriculture, Vol. 186, pp. 1-9, 2021. doi: 10.1016/j.compag.2021.106184.
• [19] 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, pp. 1-9, 2020, doi: 10.1016/j.compag.2020.105824.
• [20] A. Sparks, “blast leaf collar” Sept. 10, 2021. [Online]. Available: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/blast-leaf-collar. [Accessed: Nov. 10, 2023]
• [21] J. Chen, D. Zhang, Y. A. Nanehkaran and D. Li, “Detection of rice plant diseases based on deep transfer learning,” Journal of the Science of Food and Agriculture, Vol. 100, pp. 3246-3256, 2020. doi: 10.1002/jsfa.10365.
• [22] C.R. Rahman, P.S. Arko, M. E. Ali, M. A. I. Khan, S. H. Apon, F. Nowrin and A. Wasif, “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosystems Engineering, Vol. 194, pp. 112-120, 2020. doi: 10.1016/j.biosystemseng.2020.03.020.
• [23] A. Sparks, “Brown Spot” Sept. 15, 2021. [Online]. Available: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/brown-spot. [Accessed: Nov. 10, 2023]
• [24] M. T. Ahad, Y. Li, B. Song and T. Bhuiyan, “Comparison of CNN-based deep learning architectures for rice diseases classification,” Artificial Intelligence in Agriculture, Vol. 9, pp. 22-35, 2023. doi: 10.1016/j.aiia.2023.07.001.
• [25] H. Yang, J. Ni, , J. Gao, Z. Han and T. Luan, “A novel method for peanut variety identification and classification by Improved VGG16,” Scientific Reports, Vol. 11, pp. 1-17, 2021. doi: 10.1038/s41598-021-95240-y.
• [26] Y. Nan, J. Ju, Q. Hua, H. Zhang and B. Wang, “A-MobileNet: An approach of facial expression recognition,” Alexandria Engineering Journal, Vol. 61, pp. 4435-4444, 2022. doi: 10.1016/j.aej.2021.09.066.
• [27] X. Xu, J. Lin, Y. Tao and X. Wang, “An improved DenseNet method based on transfer learning for fundus medical images,” in 7th international conference on digital home 2018 (ICDH), pp. 137-140. Nov. 30 - Dec 01, 2018, Guilin, China [Online]. Available: IEEE Xplore, http://www.ieee.org. [Accessed: 10 Nov. 2023].
• [28] M. Mujahid, F. Rustam, R. Álvarez, J. L.V. Mazón, I. T. Díez and I. Ashraf, “Pneumonia classification from X-ray images with inception-V3 and convolutional neural network,” Diagnostics, Vol. 12, pp. 1-16, 2022. doi: 10.3390/diagnostics12051280.
• [29] K. Weiss, T. M. Khoshgoftaar and D. Wang, “A survey of transfer learning,” Journal of Big data, Vol. 3, pp. 1-40, 2016. doi: 10.1186/s40537-016-0043-6.
• [30] A. Sparks, “Bacterial Blight” Sept. 25, 2021. [Online]. Available: http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/bacterial-blight?category_id=326. [Accessed: Nov. 10, 2023]
• [31] N. A. Mohamed, N. M. F. Ngah, A. Abas, N. Talip, M. N. Sarian, H. S. Hamezah, S. Harun and H. Gunawan, “Candidate miRNAs from Oryza sativa for Silencing the Rice Tungro Viruses,” Agriculture, Vol. 13, pp. 1-14, 2023. doi: 10.3390/agriculture13030651.
• [32] N. Barışçı, M. Güllü and İ.A. Doğru, “Derin Transfer Öğrenmeye Dayalı Pirinç Bitkisi Hastalıklarının Tespiti,” in Accepted Abstracts e-Book: Proc. of the 5th International Conference on Artificial Intelligence and Applied Mathematics in Engineering, ICAIAME 2023, Antalya, Türkiye, 03-05 November 2023, pp. 40.