Accurate Analysis of Blast Disease Prediction in Rice Crop Using Artificial Neural Network Algorithm Compared with Random Forest Algorithm
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3594-5_28
Reference8 articles.
1. Shahriar Sazzad TM, Anwar A, Islam S, Mila SA, Jannat Oishee S, Anjum A (2020) A computer based image processing approach to identify rice blast. In: 2020 2nd international conference on sustainable technologies for industry 4.0 (STI), pp 1–5. https://doi.org/10.1109/STI50764.2020.9350507.2
2. Shahriar Sazzad TM, Anwar A, Hasan M, Ismail Hossain Md (2020) An image processing framework to identify rice blast. In: 2020 international congress on human-computer interaction, optimization and robotic applications (HORA), pp 1–5
3. Ramesh S, Tamilselvi M, Ramkumar G, Anitha G, Nirmala P (2022) Comparison and analysis of rice blast disease identification in greenhouse controlled environment and field environment using ML algorithms. In: 2022 international conference on advances in computing, communication and applied informatics (ACCAI), Chennai, India, pp 1–5. https://doi.org/10.1109/ACCAI53970.2022.9752538
4. Tosawad T, Kasetkasem T, Laungnarutai W, Phatrapornnant T, Kumazawa I (2022) On the use of class activation map on rice blast disease identification and localization. In: 2022 19th international conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON), pp 1–4
5. Siddarajamma B, Praveen Kumar MS, Sivaprasad N, Boranna R (2022) Machine learning-based approach to detect and classify rice blast disease. In: 2022 IEEE 2nd Mysore sub section international conference (Mysore Com), pp 1–6
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