Classification of organic quinoa crops using multispectral aerial imagery and machine learning techniques-Reference-Cited by-同舟云学术

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Classification of organic quinoa crops using multispectral aerial imagery and machine learning techniques

Published:2022-10-24 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA)
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Author:

Flores Andres¹

Affiliation:

1. Pontificia Universidad Católica del Perú,Unmanned Aircraft Systems Group,Lima,PERU

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10005873/10005896/10006196.pdf?arnumber=10006196

Reference18 articles.

1. Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

2. Use Of Deep Neural Networks For Crop Yield Prediction: A Case Study Of Soybean Yield in Lauderdale County, Alabama, USA

3. Performance Evaluation of ML and Deep Learning Algorithms in Crop Classification: Impact of Hyper-parameters and Training Sample Size;kim;Korean Journal of Remote Sensing,2018

4. Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

5. Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Infrared Detection Technologies in Smart Agriculture: A Review;2023 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2023 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM);2023-09-01

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