Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification-Reference-Cited by-同舟云学术

Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification

Published:2023 Issue: Volume:11 Page:20795-20805
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Reyana A.¹^ORCID,Kautish Sandeep²^ORCID,Karthik P. M. Sharan³^ORCID,Al-Baltah Ibrahim Ahmed⁴^ORCID,Jasser Muhammed Basheer⁵^ORCID,Mohamed Ali Wagdy⁶^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

2. Department of Computer Science and Engineering, LBEF Campus, Kathmandu, Nepal

3. Department of Civil Engineering, SSM Institute of Engineering and Technology, Dindigul, Tamil Nadu, India

4. Department of Information Technology, Faculty of Computer Science and Information Technology, Sana’a University, Sana’a, Yemen

5. Department of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya, Selangor, Malaysia

6. Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10054040.pdf?arnumber=10054040

Reference31 articles.

1. Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

2. Regional Mapping of Plantation Extent Using Multisensor Imagery

3. The Rosario dataset: Multisensor data for localization and mapping in agricultural environments

4. An efficient analysis of crop yield prediction using Hadoop framework based on random forest approach

5. Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

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