Deep learning for precision agriculture: A bibliometric analysis-Reference-Cited by-同舟云学术

Deep learning for precision agriculture: A bibliometric analysis

Published:2022-11 Issue: Volume:16 Page:200102
ISSN:2667-3053
Container-title:Intelligent Systems with Applications
language:en
Short-container-title:Intelligent Systems with Applications

Author:

Coulibaly Solemane^ORCID,Kamsu-Foguem Bernard,Kamissoko Dantouma,Traore Daouda

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,Computer Vision and Pattern Recognition,Signal Processing,Computer Science (miscellaneous)

Reference122 articles.

1. Affouard, A., Lombardo, J.-.C., Goëau, H., Bonnet, P., & Joly, A. (2019). Pl@ntNet. https://hal.archives-ouvertes.fr/hal-02096020.

2. Cotton pests classification in field-based images using deep residual networks;Alves;Computers and Electronics in Agriculture,2020

3. bibliometrix : An R-tool for comprehensive science mapping analysis;Aria;Journal of Informetrics,2017

4. Solving current limitations of deep learning based approaches for plant disease detection;Arsenovic,2019

5. Arun, P.J., .& Gopal, G. (2019). Data for : Identification of plant leaf diseases using a 9-layer deep convolutional neural network. 1. https://doi.org/10.17632/tywbtsjrjv.1.

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