TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING
Keywords:Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Leaf Disease, Crop Disease, Tomato Leaf
Tomatoes are the most extensively planted vegetable crop in India's agricultural lands. Although the tropical environment is favorable for its growth, specific climatic conditions and other variables influence tomato plant growth. In addition to these environmental circumstances and natural disasters, plant disease is a severe agricultural production issue that results in economic loss. Therefore, early illness detection can provide better outcomes than current detection algorithms. As a result, deep learning approaches based on computer vision might be used to detect diseases early. This study thoroughly examines the disease categorization and detection strategies used to identify tomato leaf diseases. The pros and limitations of the approaches provided are also discussed in this study. Finally, employing hybrid deep-learning architecture, this research provides an early disease detection approach for detecting tomato leaf disease.
Aarthi, R. & 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. https://acadpubl.eu/jsi/2018-118-7-9/articles/7/25.pdf
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. https://www.ripublication.com/ijafst_spl/ijafstv4n4spl_11.pdf
Chen, F. Kissel, D. E. Clark, R. West, L. T. Rickman, D. Laval, J. Adkin, W. (1997). Determining surface soil clay concentration at a field scale for precision agriculture, University of Georgia, Huntsville.
Cheng, P. M. & 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. &Pawar, M. (2018). Transfer Learning for Image Classification. In 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). https://www.kaggle.com/janmejaybhoi/cotton-disease-datas
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. https://acsess.onlinelibrary.wiley.com/doi/abs/10.2134/1999.precisionagproc4.c70
Hakkim, V. M. A. Joseph, E. A. Gokul, A. J. A. Mufeedha, K. (2016). Precision Farming: The Future of Indian Agriculture. https://doi.org/10.7324/JABB.2016.40609
Hana, D. Liu, Q. & 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. https://doi.org/10.11591/ijai.v8.i4.pp328-341
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, 768-771. https://doi.org/10.1109/ICCUBEA.2015.153
Larsen-Freeman, D. (2013). Transfer of Learning Transformed. Language Learning, 63, 107-129. https://doi.org/10.1111/j.1467-9922.2012.00740.x
Lazar, V. and Rodolphe, J. (n.d.). Fela Winkelmolen, and Cédric Archambeau. A Simple Transferlearning Extension of Hyperband. NeurIPS Workshop on Meta-Learning.
Lee, S. J. Chen, T. Yu, L. & 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. & Lanka, V. (2021). Cotton Leaves Disease Detection and Cure Using Deep Learning. https://www.irjmets.com/uploadedfiles/paper/volume3/issue_1_january_2021/5926/1628083243.pdf
Njoroge, J. B. Ninomiya, K. Kondo, N. (2002). Automated fruit grading system using image processing, In Proceedings of the 41st SICE Annual Conference. 1346-1351. https://ieeexplore.ieee.org/abstract/document/1195388
Pan, S. J. and Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191
Patil, B. Panchal, H. Yadav, M. S. Singh, M. A. & Patil, M. D. (2017). Plant Monitoring using image processing, Raspberry Pi & IoT. International Research Journal of Engineering and Technology (IRJET), 4(10). https://d1wqtxts1xzle7.cloudfront.net/54946853/IRJET-V4I10243-with-cover-page-v2.pdf?Expires=1655701666&Signature=X7KrjAn-ugEpT6wiLroaOP3o6BilptmHHzEF~m4-rn343rnveN-lGkSS0jvHGZdRWgZgEwjIcMFizxA9HUhUeZYeVpwhSkIuK~NNsU-R1cApmD798LwHPPYe5JOSxQdfmI0TOr1PZ3mURbSTejWMWztmhM6A3Bz4lO~GoUwa14pjB0DVUe9oDcwyETZzjxzGuRDQKa-f2~thlb89bVROm7ruJlrateb5OYkZHFrCGIc9VH4GHqrWDiRDBYNqUjBbF0yMyt2CAkH6c59gQGnovWi9MQdyuRnmY4wsT0izv-a0zZmoff1Kct~7~khIUsR0KaemDSJ5Kk1hg~NGq-6dtA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
Sannakki, S. S. et al. (2013). Using neural networks to diagnose and classify grape leaf diseases. Fourth International Conference on Computing, Communications, and Networking Technologies (ICCCNT), 1-5.
Shrivastava, S. & 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. http://article.sapub.org/10.5923.j.ajis.20140404.01.html
Zagoruyko, S. and Komodakis, N. (2017). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In International Conference on Learning Representations. https://arxiv.org/abs/1612.03928
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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