Estimating Crop Grain Yield and Seed Composition Using Deep Learning from UAV Multispectral Data-Reference-Cited by-同舟云学术

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Estimating Crop Grain Yield and Seed Composition Using Deep Learning from UAV Multispectral Data

Published:2023-07-16 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Maimaitijiang Maitiniyazi¹,Millett Bruce¹,Paheding Sidike²,Khan Shahid Nawaz¹,Dilmurat Kamila³,Reyes Abel A.²,Kovács Péter⁴

Affiliation:

1. South Dakota State University,Department of Geography and Geospatial Sciences,Brookings,USA,SD 57007

2. Michigan Technological University,Department of Applied Computing,Houghton,USA,MI 4993

3. Saint Louis University,Department of Earth and Atmospheric Sciences,St. Louis,USA,MO 63108

4. South Dakota State University,Department of Agronomy, Horticulture & Plant Science,Brookings,USA,SD 57007

Funder

Experiment

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10281802.pdf?arnumber=10281802

Reference9 articles.

1. Transformer-based deep learning for predicting protein properties in the life sciences

2. Spectral–Temporal Fusion of Satellite Images via an End-to-End Two-Stream Attention With an Effective Reconstruction Network

3. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy,2020

4. Leveraging Very-High Spatial Resolution Hyperspectral and Thermal UAV Imageries for Characterizing Diurnal Indicators of Grapevine Physiology

5. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

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