IDENTIFICATION OF TOMATO LEAF DISEASE PREDICTION USING CNN
Keywords:Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Leaf Disease, Tomato Leaf, Multiclass Classification
In India tomatoes are broadly vegetable crop. However, the tropical environment is ideal for tomato plant growth, specific climatic conditions and other factors influence tomato plant growth. Aside from these environmental factors and natural disasters, plant disease is a serious agricultural production issue that causes economic loss. As an outcome, early illness detection may produce better results than existing detection methods. As a result, deep learning approaches based on computer vision might be used to detect diseases early. The disease categorization and detection strategies used to identify tomato leaf diseases are thoroughly examined in this study. This study also discusses the benefits and drawbacks of the approaches presented. After all, using a hybrid deep-learning architecture, this study provides an early disease detection method for tomato leaf disease.
Aarthi, R., and Harini, S. (2018). "A Survey of Deep Convolutional Neural Network Applications in Image Processing". International Journal of Pure and Applied Mathematics, 118 (7), 185-190.
Abdul Hakkim, V.M, Abhilash Joseph, E., Ajay Gokul, A.J., And Mufeedha, K. (2016). Precision Farming : The Future of Indian Agriculture. J App Biol Biotech, 4 (06), 068-072. http://dx.doi.org/10.7324/JABB.2016.40609.
Adamchuk, V.I., Hummel, J.W., Morgan, M.T., Upadhyaya, S.K. (2004). On-The-Go Soil Sensors for Precision Agriculture. Computers and Electronics in Agriculture, 44, 71-91. https://doi.org/10.1016/j.compag.2004.03.002.
Arjun, K. M. (2013). Indian Agriculture Status, Importance, and Role in the Indian Economy. International Journal of Agriculture and Food Science Technology, 4(4), 343-346.
Barbedo, J.G.A. (2018). Impact of Dataset Size and Variety on the Effectiveness of Deep Learning and Transfer Learning for Plant Disease Classification. Computers and Electronics in Agriculture, 153, 46-53 (2018). https://doi.org/10.1016/j.compag.2018.08.013.
Chen, F., Kissel, D.E., Clark, R., West, L.T., Rickman, D., Luval, J., and Adkin, W. (1997). Determining Surface Soil Clay Concentration at a Field Scale for Precision Agriculture, University of Georgia, Huntsville.
Cheng, P. M., And Malhi, H. S. (2017). "Transfer Learning With Convolutional Neural Networks for Classification of Abdominal Ultrasound Images". Journal of Digital Imaging, Springer, 30(2), 234-243. https://doi.org/10.1007/s10278-016-9929-2.
Cheng, P. Shaha, M., and Pawar, M. (2018). "Transfer Learning for Image Classification". In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, 656-660. https://doi.org/10.1109/ICECA.2018.8474802.
Cotton Disease Dataset. (2020b). Kaggle.
Devikar, P. (2016)."Transfer Learning for Image Classification of Various Dog Breeds", International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), 5(12), 2707-2715.
Ferguson R., Dobermann, A. and Schepers, J. (2007). Precision Agriculture : Site-Specific Nitrogen Management for Irrigated Corn. University of Nebraska Lincoln Extension, Bulletin, 1-7.
Hana D., Liu,Q. And Fan, W.(2017). "A New Image Classification Method Using CNN Transfer Learning and Web Data Augmentation", Expert Systems with Applications, Elsevier,95, 43-56. https://doi.org/10.1016/j.eswa.2017.11.028.
Jadhav, S. B. (2019). Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification IAES International Journal of Artificial Intelligence, 8(4), 328. http://doi.org/10.11591/ijai.v8.i4.pp328-341.
Chen, K., Zhu, H., Yan, L. and Wang, J. (2020). "A Survey on Adversarial Examples in Deep Learning, "Journal on Big Data, 2(2), 71-84. https://doi.org/10.32604/jbd.2020.012294.
Khirade, S.D. and Patil, A.B. (2015)."Plant Disease Detection Using Image Processing." ICCUBEA (International Conference on Computing, Communication, Control, and Automation) is a Biennial International Conference on Computing, Communication, Control, and Automation.), IEEE. 768-771. https://doi.org/10.1109/ICCUBEA.2015.153.
Lee, S. J., Chen, T., Yu, L., and Lai, C. H. (2018). "Image Classification Based on the Boost Convolutional Neural Network". IEEE Access, 6, 12755-12768. https://doi.org/10.1109/ACCESS.2018.2796722.
Mhatre, R., and Lanke, V. (2021). Cotton Leaves Disease Detection and Cure Using Deep Learning. International Research Journal of Modernization In Engineering Technology and Science, 3(1), 1-6.
Njoroge, J.B., Ninomiya, K., and Kondo, N. (2002). Automated Fruit Grading System Using Image Processing, in Proceedings of the 41st SICE Annual Conference. 2, 1346-1351. https://doi.org/10.1109/SICE.2002.1195388.
Patil, B., Panchal, H., Yadav, M. S., Singh, M. A., and Patil, M. D. (2017). Plant Monitoring Using Image Processing, Raspberry Pi and IoT. International Research Journal of Engineering And Technology (IRJET), 4(10).
Sannakki, S.S., Rajpurohit, V. S., Nargund, V. B. And Kulkarni, P. (2013)."Using Neural Networks to Diagnose and Classify Grape Leaf Diseases." 2013 Fourth International Conference on Computing, Communications, and Networking Technologies (ICCCNT). IEEE, 1-5.
Shrivastava, S., and Hooda, D. S. (2014). Automatic Brown Spot and Frog Eye Detection from the Image Captured in the Field. American Journal of Intelligent Systems, 4(4), 131-134.
Song, C., Cheng, X., Gu, Y., Chen, B., and Fu, Z. (2020). "A Review Of Object Detectors in Deep Learning, "Journal on Artificial Intelligence, 2 (2), 59-77. https://doi.org/10.32604/jai.2020.010193.
Sun, X., Mu, S., Xu, Y., Cao, Z. and Su, T. (2019). "Image Recognition of Tea Leaf Diseases Based on Convolutional Neural Network. International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 304-309. https://doi.org/10.1109/SPAC46244.2018.8965555.
Wu, H., Liu, Q., and Liu, X. (2019). "A Review on Deep Learning Approaches to Image Classification and Object Segmentation," Computers, Materials and Continua, 60 (2), 575-597. https://doi.org/10.32604/cmc.2019.03595.
Zhou, C., Zhou, S., Xing, J. and Song, J., (2021). Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network IEEE Access, 9, 28822-28831. https://doi.org/10.1109/ACCESS.2021.3058947.
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Copyright (c) 2022 Niharika Saxena, Dr. Neha Sharma
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