PDS-MCNet: a hybrid framework using MobileNetV2 with SiLU6 activation function and capsule networks for disease severity estimation in plants
Author:
Funder
DST, MST, Govt. of India.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08693-9.pdf
Reference63 articles.
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3. Verma S, Chug A, & Singh AP (2020) Recent advancements in image-based prediction models for diagnosis of plant diseases. In: Proceedings of 3rd international conference on computer vision and image processing (pp. 365–377). Springer, Singapore
4. Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90
5. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21(2):110–124
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1. PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning;Frontiers in Plant Science;2024-01-08
2. TomSevNet: a hybrid CNN model for accurate tomato disease identification with severity level assessment;Neural Computing and Applications;2023-12-26
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