Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches-Reference-Cited by-同舟云学术

Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks and Attention Convolutional LSTM Approaches

Published:2023 Issue: Volume:16 Page:254-266
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Nejad Seyed Mahdi Mirhoseini¹^ORCID,Abbasi-Moghadam Dariush¹^ORCID,Sharifi Alireza²^ORCID,Farmonov Nizom³^ORCID,Amankulova Khilola³^ORCID,Laszlz Mucsi³^ORCID

Affiliation:

1. Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2. Surveying Engineering, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

3. Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Szeged, Hungary

Funder

University of Szeged Open Access Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/09956008.pdf?arnumber=9956008

Reference50 articles.

1. Crop yield prediction integrating genotype and weather variables using deep learning

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3. Gradient-based learning applied to document recognition

4. Batch normalization: Accelerating deep network training by reducing internal covariate shift;ioffe;Proc 32nd Int Conf Mach Learn,0

5. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data

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