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Identification and Classification of Rice Plant Diseases using Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 53 |
| Year of Publication: 2022 |
| Authors: Jyoti Dinkar Bhosale, Lomte Santosh S., Prasadupeedi |
10.5120/ijca2022921949
|
Jyoti Dinkar Bhosale, Lomte Santosh S., Prasadupeedi . Identification and Classification of Rice Plant Diseases using Machine Learning. International Journal of Computer Applications. 183, 53 ( Feb 2022), 18-23. DOI=10.5120/ijca2022921949
Abstract
In this paper Plant disease identification is crucial for preventing reductions in agricultural output quantity and production. To ease agricultural issues, several machine learning and image processing technologies are applied. This review is mostly concerned with rice plant disease. Detection based on image inputs from ill rice plants using various ML and image processing algorithms. Furthermore, the important ML and image processing concepts in plant identification and categorization there has been mention of sicknesses. Probabilistic Neural Network (PNN), Evolutionary Techniques (GA), & k-Nearest Neighbor (K-Nearest Neighbor) are three classification algorithms. Neighbor Classifier (KNN) & Support Vector Machine (SVM) are two more (SVM). The reliability of an output relies on the input data when used in a number of agricultural research applications. As a consequence, selecting a categorizing approach is a major duty. Agriculture, biological research, and so forth. Are there other industries that employ leaf disease classification? Comprehensive research into rice plant diseases, image dataset size, processing & segmentation methodologies, or classifiers are all important variables to consider.
References
- Mead, G. C. (2007). Microbiological analysis of red meat, poultry and eggs. 348.
- Yousef, A. E. (2010). Analytical food microbiology.
- Kornacki, J. L. (Jeffrey L. (2010). Principles of microbiological troubleshooting in the industrial food processing environment. 193.
- Revathi, P., Revathi, R., & Hemalatha, M. (2011). Comparative Study of Knowledge in Crop Diseases Using Machine Learning Techniques. International Journal of Computer Science and Information Technologies (IJCSIT), 2(5), 2180–2182.
- Taormina, P. J. (2012). Microbiological research and development for the food industry. Microbiological Research and Development for the Food Industry, 1–327.
- Prajapati, B. S., Dabhi, V. K., &Prajapati, H. B. (2016). A survey on detection and classification of cotton leaf diseases. International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016,2499–2506 .
- Prajapati, H. B., Shah, J. P., &Dabhi, V. K. (2017). Detection and classification of rice plant diseases. Intelligent Decision Technologies, 11(3), 357–373.
- Ganatra, N., & Patel, A. (2018). A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning. International Journal of Computer Applications, 180(13),7–12.
- Behera, S. K., Jena, L., Roth, A. K., &Seth, P. K. (2018). Disease Classification and Grading of Orange Using Machine Learning and Fuzzy Logic. Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018, 678–682.
- Srinivas, B., &Rao, G. S. (2019). To Secure Your Paper As Per UGC Guidelines We Are Providing An Electronic Bar Code. 09(June).
- Sherly Puspha Annabel, L., Annapoorani, T., &Deepalakshmi, P. (2019). Machine learning for plant leaf disease detection and classification - A review. Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 538–542.
- Doh, B., Zhang, D., Shen, Y., Hussain, F., Doh, R. F., &Ayepah, K. (2019). Automatic citrus fruit disease detection by phenotyping using machine learning. ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing, September, 1–5.
- Daniya, T., &Vigneshwari, S. (2019). A review on machine learning techniques for rice plant disease detection in agricultural research. International Journal of Advanced Science and Technology, 28(13), 49–62.
- Swain, S., Nayak, S. K., &Barik, S. S. (2020). A Review on Plant Leaf Diseases Detection and Classification Based on Machine Learning Models SasmitaKumariNayak Swati SucharitaBarik. MuktShabd Journal, 9(6), 5195–5205.
- Gobalakrishnan, N., Pradeep, K., Raman, C. J., Ali, L. J., &Gopinath, M. P. (2020). A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases. Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020, 465–468.
- Ganatra, N., & Patel, A. (2020). A multiclass plant leaf disease detection using image processing and machine learning techniques. International Journal on Emerging Technologies, 11(2), 1082–1086.
- Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., Wang, F., Zhou, M., & Liu, W. (2020). A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network. Sensors(Switzerland),20(3).
- Kartikeyan, P., & Shrivastava, G. (2021). Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning. International Journal of Computer Applications, 174(11),39–48.
- Shrivastava, V. K., &Pradhan, M. K. (2021). Rice plant disease classification using color features: a machine learning paradigm. Journal of Plant Pathology, 103(1), 17–26.
- WHO, F. A. O. (n.d.). Statistical Aspects of Microbiological Criteria Related to Foods.
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