Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation-Reference-Cited by-同舟云学术

Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation

Published:2022-07 Issue:3 Volume:19 Page:1352-1364
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Avigal Yahav¹^ORCID,Wong William¹^ORCID,Presten Mark¹,Theis Mark¹,Aeron Shrey¹^ORCID,Deza Anna¹,Sharma Satvik¹,Parikh Rishi¹,Oehme Sebastian²,Carpin Stefano³^ORCID,Viers Joshua H.⁴,Vougioukas Stavros⁵^ORCID,Goldberg Ken Y.¹^ORCID

Affiliation:

1. AUTOLAB, UC Berkeley, Berkeley, CA, USA

2. Department of Electrical and Computer Engineering, TU Munich, München, Germany

3. School of Engineering, UC Merced, Merced, CA, USA

4. Department of Environmental Systems, School of Engineering, UC Merced, Merced, CA, USA

5. Department of Biological and Agricultural Engineering, UC Davis, Davis, CA, USA

Funder

RAPID: Robot-Assisted Precision Irrigation Delivery Project through the NSF National Robotics Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielam/8856/9814439/9684020-aam.pdf

Reference32 articles.

1. ADADELTA: An adaptive learning rate method;zeiler;arXiv 1212 5701,2012

2. Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images

3. Modelling individual growth and competition in plant populations: growth curves of Chenopodium album at two densities

4. AN INDIVIDUAL-PLANT GROWTH SIMULATION MODEL FOR QUANTIFYING PLANT COMPETITION

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