Prediction of rice disease using modified feature weighted fuzzy clustering (MFWFC) based segmentation and hybrid classification model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-022-01835-7.pdf
Reference23 articles.
1. Ahmed K, Shahidi TR, Alam SMI, Momen S (2019) Rice leaf disease detection using machine learning techniques. In: 2019 international conference on sustainable technologies for industry 4.0 (STI). IEEE, pp 1–5
2. Al-Amin M, Karim DZ, Bushra TA (2019). Prediction of rice disease from leaves using deep convolution neural network towards a digital agricultural system. In: 2019 22nd international conference on computer and information technology (ICCIT). IEEE, pp 1–5
3. Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, Majeed APA (2021) A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput Sci 7:e432
4. Bashir K, Rehman M, Bari M (2019) Detection and classification of rice diseases: an automated approach using textural features. Mehran Univ Res J Eng Technol 38(1):239–250
5. Bera T, Das A, Sil J, Das AK (2019) A survey on rice plant disease identification using image processing and data mining techniques. Emerging technologies in data mining and information security. Springer, Singapore, pp 365–376
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