Identification of Rice Plant Disease Using Convolution Neural Network Inception V3 and Squeeze Net Models
Keywords:
Convolution Neural Network (CNN), Inception V3, SqueezeNet, Deep Learning (DL)Abstract
Agriculture is the most important factor of every country. Crop disease is one of the reasons to reduce the crop yield. So detect the crop disease at early age yield help to improve crop production. In India, Rice is one of the important foods. Different products are made from rice, so it also helps to improve economy of country. But due to different disease found on rice plant, causes the loss of production, which also affects the economy of country. Mostly bacterial blight, blast and brownspot are the diseases found on the rice plant. Researcher developed different deep learning techniques for crop disease identification, which worked on different dataset of crops. In this paper different rice plant diseases are described. The CNN Inception V3 and SqueezeNet model are used for rice crop disease identification on publically available dataset and comparison of both the module given the paper.
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Ruoling Deng1 , Ming Tao1 , Hang Xing1 , Xiuli Yang1 , Chuang Liu1 , Kaifeng Liao1 and Long Qi, “Automatic Diagnosis of Rice Diseases Using Deep Learning”, Frontiers in Plant Science, 2021
Md. Mafiul Hasan Matin, Amina Khatun, Md. Golam Moazzam, Mohammad Shorif Uddin, “An
Ghazanfar Latif, Sherif E. Abdelhamid , Roxane Elias Mallouhy , Jaafar Alghazo and Zafar Abbas Kazimi, “Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model” Plants 2022
T. Daniya, S. Vigneshwari, “Deep Neural Network for Disease Detection in Rice Plant Using the Texture and Deep Features”, Computational Intelligence, Machine Learning and Data Analytics The Computer Journal, Vol. 00 No. 00, 2021
Alvaro Fuentes , Sook Yoon3 , Mun Haeng Lee and Dong Sun Park, “Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes”, Frontiers in Plant Science, 2021
C. S., S., Kunju, N. ., & Shahul Hameed, T. A. . (2023). Design and Simulation of Two Stage Wideband CMOS Amplifier in 90 NM Technology. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 249–258. https://doi.org/10.17762/ijritcc.v11i2s.6144
Junde Chena , Jinxiu Chena , Defu Zhanga , Yuandong Sunb , Y.A. Nanehkarana, “Using deep transfer learning for image-based plant disease identification”, 2020 Published by Elsevier B.V
Lei Feng, Baohua Wu, Susu Zhu, Junmin Wang , Zhenzhu Su , Fei Liu, Yong He and Chu Zhang, “Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods”, Frontiers in Plant Science, 2020
[8] Feng Jiang , Yang Lu , Yu Chen , Di Cai , Gongfa Li, “Image recognition of four rice leaf diseases based on deep learning and support vector machine”, 2020 Elsevier B.V
Javaid Ahmad Wani, parsh Sharma, Malik Muzamil, Suhaib Ahmed, Surbhi Sharma, Saurabh Singh, “Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges”, springer 2021
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, “Rethinking the Inception Architecture for Computer Vision”, IEEE, 2016
Edna Chebet Tooa , Li Yujiana , Sam Njukia , Liu Yingchunb, “A comparative study of fine-tuning deep learning models for plant disease identification”, 2018 Elsevier B.V
Prof. Sharayu Waghmare. (2012). Vedic Multiplier Implementation for High Speed Factorial Computation. International Journal of New Practices in Management and Engineering, 1(04), 01 - 06. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/8
Nikhitha M, Roopa Sri S, Uma Maheswari B, “Fruit Recognition and Grade of Disease Detection using Inception V3 Model”, IEEE 2019
Vimal K. Shrivastava , Monoj K. Pradhan, Sonajharia Minz , Mahesh P. Thakur, “Rice Plant Disease Classification Using Transfer Learning Of Deep Convolution Neural Network”, ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019, pp.631-635
Rutuja R. Patil And Sumit Kumar, “Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis”, IEEE Access 2022
Ashutosh Kumar Singh , SVN Sreenivasu , U.S.B. K. Mahalaxmi , Himanshu Sharma , Dinesh D. Patil , and Evans Asenso, “Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier”, Hindawi Journal of Food Quality 2022
Mr. Dharmesh Dhabliya, Mr. Rahul Sharma. (2012). Efficient Cluster Formation Protocol in WSN. International Journal of New Practices in Management and Engineering, 1(03), 08 - 17. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/7
Ms. Priya Ujawe, Dr. Smita Nirkhi (2022), “Comparative study of Tomato Crop Disease Detection System using Deep Learning Techniques”, Intelligent Communication Technologies and Virtual Mobile Networks: Proceedings of ICICV 2022, Springer
S. Ramaneswaran , Kathiravan Srinivasan , P. M. Durai Raj Vincent , and Chuan-Yu Chang, “Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification”, Hindawi Computational and Mathematical Methods in Medicine Volume 2021
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