Abstract

The purpose of this work is to use the artificial intelligence features of the ResNet50 architecture to provide a novel method of paddy disease identification. Farmers face numerous problems in raising paddy as its yield is affected by various factors like changing biodiversity, environment, weather pests, and disease. Traditional methods combined with smart farming, innovation, tools, and technology are needed for the mass production of food Here we develop a model using a convolutional neural network, ResNet50 that identifies disease in paddy leaf. The proposed model paddy disease identification model will give more precise results. The paddy disease identification model may be transformed into TensorFlow Lite (TFLite), which can be used for Android phones and drone applications, among other things. The Paddy model in this article obtained a training accuracy of almost 99% and a test accuracy of 92.83% when it was trained on 13,876 well-defined datasets. The loss function of 0.0014 at 100 epochs demonstrated that the model was effectively trained using ResNet50.

Keywords

Paddy Model, Paddy CNN, Drones, Leaf Classification, Smart Farming,

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References

  1. H. U Awall Rezvi, M. Tahjib-Ul-Arif, M.A. Azim, T.A. Tumpa, M.M. Hasan Tipu, F. Najnine, A. M.F. Dawood, M. Skalicky and M. Brestic, Rice and food security: Climate change implications and the prospects for nutritional security. Food and Energy Security, 12(1), (2022) e430. https://doi.org/10.1002/fes3.430
  2. M.S. Hunjan, J.S. Lore, Climate Change: Impact on Plant Pathogens, Diseases, and Their Management. Crop Protection Under Changing Climate, (2020) 85-100. https://doi.org/10.1007/978-3-030-46111-9_4
  3. Sustainable Food and Agriculture (no date) Food and Agriculture Organization of the United Nations. Available at: https://www.fao.org/agriculture-consumer-protection-department/en/
  4. N.A. Mohidem, N. Hashim, R. Shamsudin and H. Che Man, Rice for Food Security: Revisiting Its Production, Diversity, Rice Milling Process and Nutrient Content. Agriculture, 12, (2022) 741. https://doi.org/10.3390/agriculture12060741
  5. K. Neupane, F. Baysal-Gurel, Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sensing, 13(19), (2021) 3841. https://doi.org/10.3390/rs13193841
  6. S.C. Chukwu, M.Y. Rafii, S.I. Ramlee, Bacterial leaf blight resistance in rice: a review of conventional breeding to molecular approach. Molecular biology reports 46, (2019) 1519–1532. https://doi.org/10.1007/s11033-019-04584-2
  7. Tnau Agritech portal, Agricultural crops :: Cereals :: Paddy Available at: https://agritech.tnau.ac.in/crop_protection/crop_prot_crop%20diseases_cereals_paddy.html
  8. M.S. Ngalimat, E.M. Hata, D. Zulperi, S.I. Ismail, M.R. Ismail, N.A.I. Mohd Zainudin, N.B. Saidi, M.T. Yusof, Streptomyces-mediated growth enhancement and Bacterial Panicle Blight disease suppression in rice plants under greenhouse conditions. Journal of Biotechnology, 359, (2022) 148-160. https://doi.org/10.1016/j.jbiotec.2022.09.018
  9. P.K. Sethy, B. Negi, N.K. Barpanda, S.K. Behera and A.K. Rath, Measurement of Disease Severity of Rice Crop Using Machine Learning and Computational Intelligence. Cognitive Science and Artificial Intelligence, (2018) 1-11. https://doi.org/10.1007/978-981-10-6698-6_1
  10. W.L. Chen, Y.B. Lin, F.L. Ng, C.Y. Liu, Y.W. Lin, RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies. IEEE Internet of Things Journal, 7(2), (2020) 1001-1010. https://doi.org/10.1109/JIOT.2019.2947624
  11. V.K. Shrivastava, M.K. Vimal, S. Minz, M.P. Thakur, Rice Plant Disease Classification using Transfer Learning of Deep Convolution Neural Network. International Archives of the Photogrammetry. Remote Sensing & Spatial Information Sciences, 42, (2019) 631-635. https://doi.org/10.5194/isprs-archives-XLII-3-W6-631-2019
  12. M.A. Islam, M.N.R. Shuvo, M. Shamsojjaman, S. Hasan, M.S. Hossain and T. Khatun, An automated convolutional neural network-based approach for paddy leaf disease detection. International Journal of Advanced Computer Science and Applications, 12(1), (2021) 280-288. https://dx.doi.org/10.14569/IJACSA.2021.0120134
  13. N. Bharanidharan, S.R.S. Chakravarthy, H. Rajaguru, V.V. Kumar, T.R. Mahesh, S. Guluwadi, Multiclass Paddy Disease Detection Using Filter-Based Feature Transformation Technique. IEEE Access, 11, (2023) 109477-109487. https://dx.doi.org/10.1109/ACCESS.2023.3322587
  14. R. Naga Swetha, V. Shravani, Monitoring of rice plant for disease detection using machine learning. International Journal of Engineering and Advanced Technology, 9(3), (2020) 851-853. https://doi.org/10.35940/ijeat.C5308.029320
  15. V.G. Biradar, H. Sarojadevi, J. Shalini, R.S. Veena, V. Prashanth, Rice Leaves Disease Classification Using Deep Convolutional Neural Network. International Journal of Health Sciences, 4, (2022) 1230-1244. https://doi.org/10.53730/ijhs.v6nS4.6067
  16. A.K. Abasi, S.N. Makhadmeh, O.A. Alomari, M. Tubishat and H.J. Mohammed, Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach. Sustainability, 15(20), (2023) 15039. https://doi.org/10.3390/su152015039
  17. A. Haridasan, J. Thomas, E.D. Raj, Deep learning system for paddy plant disease detection and classification. Environmental monitoring and assessment, 195(1), (2023) 120. https://doi.org/10.1007/s10661-022-10656-x
  18. N. Senan, M. Aamir, R. Ibrahim, N.S. Taujuddin, W.H. Muda, An Efficient Convolutional Neural Network for Paddy Leaf Disease and Pest Classification. International Journal of Advanced Computer Science and Applications, 11(7), 2020. https://doi.org/10.14569/IJACSA.2020.0110716
  19. K.E. Salman, S.E. Badr, E.K. Ghoniem, A.A. Aboulila, A. Amero Emeran, Role of silymarin induced rice immunity against blast pathogen Magnaporthe oryzae through regulation of resistance genes expression. Physiological and Molecular Plant Pathology, 115, (2021) 101678. https://doi.org/10.1016/j.pmpp.2021.101678
  20. H. Kato, Rice blast disease. Pesticide Outlook, 12(1), (2001) 23-25. https://doi.org/10.1039/b100803j
  21. A.E. Asibi, Q. Chai, J.A. Coulter, Rice Blast: A Disease with Implications for Global Food Security. Agronomy, 9(8), (2019) 451. https://doi.org/10.3390/agronomy9080451
  22. R. Singh, S. Sunder, P. Kumar, Sheath blight of rice: current status and perspectives. Indian Phytopathol, 69(4), (2016) 340-351.
  23. S. Huded, D. Pramesh, A. Chittaragi, S. Sridhara, E. Chidanandappa, M.K. Prasannakumar, C. Manjunatha, B. Patil, S. Shil, H.D. Pushpa, Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India. Agronomy, 12(12), (2022) 2947. https://doi.org/10.3390/agronomy12122947
  24. N.K. Chakrabarti, Epidemiology and Disease Management of Brown Spot of Rice in India, In: Major Fungal Diseases of Rice, Major fungal diseases of rice: recent advances, (2001) 293-306. https://doi.org/10.1007/978-94-017-2157-8_21
  25. M. Surendhar, Y. Anbuselvam, J. Ivin Subakar Johnny, Status of Rice Brown Spot (Helminthosporium oryza) Management in India: A Review. Agricultural Reviews, 43(2), (2022), 217-222. https://doi.org/10.18805/ag.R-2111
  26. R. Meena, S. Joshi, S. Raghuwanshi, Detection of Varieties of Diseases in Rice Plants using Deep Learning Techniques. 4th International Conference on Inventive Research in Computing Applications, (2022) 664-674. https://doi.org/10.1109/ICIRCA54612.2022.9985745
  27. A. Gupta, B. Liu, J. Chen, B. Yang, High-efficiency prime editing enables new strategies for broad-spectrum resistance to bacterial blight of rice. Plant Biotechnology Journal, 21(7), (2023) 1454-1464. https://doi.org/10.1111/pbi.14049
  28. P. Tejaswini, P. Singh, M. Ramchandani, Y.K. Rathore, R.R. Janghel, Rice Leaf Disease Classification Using Cnn. IOP Conference Series: Earth and Environmental Science, 1032(1), (2022) 012017. https://dx.doi.org/10.1088/1755-1315/1032/1/012017
  29. T. Gayathri Devi, P. Neelamegam, Image processing-based rice plant leaves diseases in Thanjavur, Tamilnadu. Cluster Computing, 22(6), (2019) 13415-13428. https://doi.org/10.1007/s10586-018-1949-x
  30. C. Nancy, S. Kiran, Cucumber Leaf Disease Detection using GLCM Features with Random Forest Algorithm. International Research Journal of multidisciplinary Technovation, 6(1), (2024) 40-50. https://doi.org/10.54392/irjmt2414
  31. T.S. Poornappriya, R. Gopinath, Rice plant disease identification using artificial intelligence approaches. International Journal of Electrical Engineering and Technology, 11(10), (2022) 392-402.http://dx.doi.org/10.34218/IJEET.11.10.2020.050
  32. H. Kukadiyaa, D. Mevaa, N. Arorab, S. Srivastava, Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks. International Research Journal of multidisciplinary Technovation, 6(1), (2024) 17-31. https://doi.org/10.54392/irjmt2412
  33. S.K. Upadhyay, A. Kumar, A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology, 14, (2022) 185-199. https://doi.org/10.1007/s41870-021-00817-5