Machine Learning in Plant Disease Research-Reference-Cited by-同舟云学术

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Machine Learning in Plant Disease Research

Published:2022 Issue: Volume: Page:299-311
ISSN:
Container-title:Machine Learning in Biological Sciences
language:
Short-container-title:

Author:

Ghosh Shyamasree,Dasgupta Rathi

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-8881-2_34

Reference23 articles.

1. Abu-Aqil G, Tsror L, Shufan E, Adawi S, Mordechai S, Huleihel M, Salman A (2020) Differentiation of Pectobacterium and Dickeya spp. phytopathogens using infrared spectroscopy and machine learning analysis. J Biophotonics 13:e201960156

2. DeChant C, Wiesner-Hanks T, Chen S, Stewart EL, Yosinski J, Gore MA, Nelson RJ, Lipson H (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107(11):1426–1432

3. Guo L, Zhao G, Xu JR, Kistler HC, Gao L, Ma LJ (2016) Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum. New Phytol 211(2):527–541

4. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv:1512.03385

5. Huang KY (2007) Applications of artificial neural network for detecting phalaenopsis seedling disease using color and texture features. Comput Electron Agric 57:3–11

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1. Hybrid CNN & Random Forest Model for Effective Mint Green Leaf Disease Diagnosis;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

2. Harnessing the future: cutting-edge technologies for plant disease control;Journal of Plant Protection Research;2023-12-15

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