An intelligent convolution based graph cut segmentation for potato leaf disease severity prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16718-7.pdf
Reference43 articles.
1. Niazi M, Rahbar K, Sheikhan M et al (2022) Entropy-based kernel graph cut for textural image region segmentation. Multimed Tools Appl 81:13003–13023. https://doi.org/10.1007/s11042-022-12005-z
2. Sai Reddy B, Neeraja S (2022) Plant leaf disease classification and damage detection system using deep learning models. Multimed Tools Appl 81:24021–24040. https://doi.org/10.1007/s11042-022-12147-0
3. Chaibou MS, Conze PH, Kalti K et al (2020) Learning contextual superpixel similarity for consistent image segmentation. Multimed Tools Appl 79:2601–2627. https://doi.org/10.1007/s11042-019-08391-6
4. Stateczny A, Narahari SC, Vurubindi P, Guptha NS, Srinivas K (2023) Underground water level prediction in remote sensing images using improved hydro index value with ensemble classifier. Remote Sens 15(8):2015. https://doi.org/10.3390/rs15082015
5. Ahmed ST, Guptha NS, Lavanya NL, Basha SM, Fathima AS (2022) Improving medical image pixel quality using MICQ unsupervised machine learning technique. Malays J Comput Sci 53-64. https://doi.org/10.22452/mjcs.sp2022no2.5
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