A big data driven vegetation disease and pest region identification method based on self supervised convolutional neural networks and parallel extreme learning machines-Reference-Cited by-同舟云学术

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A big data driven vegetation disease and pest region identification method based on self supervised convolutional neural networks and parallel extreme learning machines

Published:2024-05 Issue: Volume:36 Page:100444
ISSN:2214-5796
Container-title:Big Data Research
language:en
Short-container-title:Big Data Research

Author:

Jiang Bo^ORCID,Wang Hao,Ma Hanxu

Publisher

Elsevier BV

Reference22 articles.

1. Integration of tillage indices and textural features of Sentinel-2A multispectral images for maize residue cover estimation;Xiang;Soil Till. Res.,2022

2. A VI-based phenology adaptation approach for rice crop monitoring using UAV multispectral images;Yang;Field Crops Res.,2022

3. A two-branch convolutional neural network based on multi-spectral entropy rate superpixel segmentation for hyperspectral image classification;Mu;Remote Sens.,2022

4. Estimation of vegetation fraction using RGB and multispectral images from UAVs;Mariana;Int. J. Remote Sens.,2020

5. Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy;Mo;Infrared Phys. Technol.,2020

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