A Hybrid Machine Learning Model to Recognize and Detect Plant Diseases in Early Stages

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Published: Aug 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9s.7390
Keywords:
Plant disease, ML, ResNet, Xception network, KNN, CNN, AlexNet, SqueezeNet v1.1 models

Main Article Content

Sudeepthi Govathoti
Assistant Professor, Department of CSE, GITAM University, Hyderabad, India
Deepthi Kamidi
Assistant Professor, Department of CSE, Vignan Institute of Technology and Science, Hyderabad
Y. Madhavi Reddy
Assistant professor, H&S (Mathematics), CVR college of Engineering, Hyderabad, India
Mahesh Kotha
Associate professor, Department of CSE (AI&ML), CMR Technical Campus, Hyderabad
Gangolu Yedukondalu
Professor, CSE (AI&ML) Department, Vignana Bharathi Institute of Technology, Hyderabad, Telangana.
Y. Krishna Bhargavi
Associate Professor, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad
Bh. Prashanthi
Assistant professor, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad, India

Abstract

This paper presents an improved Inception module to recognise and detect plant illnesses substituting the original convolutions with architecture based on modified-Xception (m-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach is capable of achieving the specified level of performance, with an average recognition fineness of 99.73 on the public dataset and 98.05 on the domestic dataset, respectively.

Article Details

How to Cite
Govathoti, S. ., Kamidi, D. ., Reddy, Y. M. ., Kotha, M. ., Yedukondalu, G. ., Bhargavi, Y. K. ., & Prashanthi, B. . (2023). A Hybrid Machine Learning Model to Recognize and Detect Plant Diseases in Early Stages. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 04–15. https://doi.org/10.17762/ijritcc.v11i9s.7390
Issue
Vol. 11 No. 9s (2023)
Section
Articles

References

Darwish, D. Ezzat, A. E. Hassanien, An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis, Swarm and Evolutionary Computation 52 (2020) 100616.

S. H. Lee, H. Go¨eau, P. Bonnet, A. Joly, New perspectives on plant disease characterization based on deep learning, Computers and Electronics in Agriculture 170 (2020) 105220.

S. Radhakrishnan, An improved machine learning algorithm for predicting blast disease in paddy crop, Materials Today: Proceedings (2020).

S. Hern´andez, J. L. L´opez, Uncertainty quantification for plant disease detection using bayesian deep learning, Applied Soft Computing 96 (2020) 106597.

T. R. Gadekallu, D. S. Rajput, M. P. K. Reddy, K. Lakshmanna, S. Bhattacharya, S. Singh, A. Jolfaei, M. Alazab, A novel pca–whale optimization-based deep neural network model for classification of tomato plant diseases using gpu, Journal of Real-Time Image Processing (2020) 1–14.

Z. Iqbal, M. A. Khan, M. Sharif, J. H. Shah, M. H. ur Rehman, K. Javed, An automated detection and classification of citrus plant diseases using image processing techniques: A review, Computers and electronics in agriculture 153 (2018) 12–32.

J. G. A. Barbedo, 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 (2018) 46–53.

Parjane, V. A. ., Arjariya, T. ., & Gangwar, M. . (2023). Corrosion Detection and Prediction for Underwater pipelines using IoT and Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 293 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2626

A. Abdalla, H. Cen, A. El-manawy, Y. He, Infield oilseed rape images segmentation via improved unsupervised learning models combined with supreme color features, Computers and Electronics in Agriculture 162 (2019) 1057–1068.

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

J. G. Barbedo, Factors influencing the use of deep learning for plant disease recognition, Biosystems engineering 172 (2018) 84–91.

D. Hughes, M. Salath´e, et al., An open access repository of images on plant health to enable the development of mobile disease diagnostics, arXiv preprint arXiv:1511.08060 (2015).

A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, NIPS’12, Curran Associates Inc., 2012, p. 1097–1105.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Computer Vision and Pattern Recognition (CVPR), 2015. URL http://arxiv.org/abs/1409.4842

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al., Imagenet large scale visual recognition challenge, International journal of computer vision 115 (3) (2015) 211–252.

S. Zhang, S. Zhang, C. Zhang, X. Wang, Y. Shi, Cucumber leaf disease identification with global pooling dilated convolutional neural network, Computers and Electronics in Agriculture 162 (2019) 422–430.

Z. Tang, J. Yang, Z. Li, F. Qi, Grape disease image classification based on lightweight convolution neural networks and channelwise attention, Computers and Electronics in Agriculture 178 (2020) 105735.

Y. Li, J. Nie, X. Chao, Do we really need deep cnn for plant diseases identification?, Computers and Electronics in Agriculture 178 (2020) 105803.

Z. Li, Y. Yang, Y. Li, R. Guo, J. Yang, J. Yue, A solanaceae disease recognition model based on seinception, Computers and Electronics in Agriculture 178 (2020) 105792.

M. Agarwal, S. K. Gupta, K. Biswas, Development of efficient cnn model for tomato crop disease identification, Sustainable Computing: Informatics and Systems 28 (2020) 100407.

W. Zeng, M. Li, Crop leaf disease recognition based on self-attention convolutional neural network, Computers and Electronics in Agriculture 172 (2020) 105341.

J. Chen, W. Wang, D. Zhang, A. Zeb, Y. A. Nanehkaran, Attention embedded lightweight network for maize disease recognition, Plant Pathology 70 (3) (2021) 630–642.

J. Chen, D. Zhang, A. Zeb, Y. A. Nanehkaran, Identification of rice plant diseases using lightweight attention networks, Expert Systems with Applications 169 (2021) 114514.

C. Szegedy, Scene classification with inception-7, 2015.

Asfaqur Rahman, M., Shahriar Nawal Shoumik, M., Mahbubur Rahman, M., Hasna Hena, M., 2021. Rice disease detection based on image processing technique, in: Smart Trends in Computing and Communications: Proceedings of SmartCom 2020, Springer. pp. 135–145.

Singla, P., Sharma, R., Kukreja, V., Bansal, A., et al., 2022a. Deep learning based multi-classification model for rice disease detection, in: 2022 10th In ternational Conference on Reliability, Infocom Technologies and Optimisation (Trends and Future Directions)(ICRITO), IEEE. pp. 1–5

Upadhyay, S.K., Kumar, A., 2022. A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology 14, 185–199.

A. Karlekar, A. Seal, Soynet: Soybean leaf diseases classification, Computers and Electronics in Agriculture 172 (2020) 105342.

Sutaji, D., Y?ld?z, O., 2022. Lemoxinet: Lite ensemble mobilenetv2 and xception models to predict plant disease. Ecological Informatics , 101698.