Soil sensing and machine learning reveal factors affecting maize yield in the mid‐Atlantic United States-Reference-Cited by-同舟云学术

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Soil sensing and machine learning reveal factors affecting maize yield in the mid‐Atlantic United States

Published:2022-12-19 Issue:1 Volume:115 Page:181-196
ISSN:0002-1962
Container-title:Agronomy Journal
language:en
Short-container-title:Agronomy Journal

Author:

Kinoshita Rintaro¹²^ORCID,Tani Masayuki²,Sherpa Sonam¹³,Ghahramani Afshin⁴,van Es Harold M.¹

Affiliation:

1. School of Integrative Plant Science, Soil and Crop Sciences Section Cornell Univ. Ithaca NY 14853‐1901 USA

2. Research Center for Global Agromedicine Obihiro Univ. of Agriculture and Veterinary Medicine Obihiro Hokkaido 080‐8555 Japan

3. International Maize and Wheat Improvement Center (CIMMYT) Patna Bihar 800025 India

4. Centre for Sustainable Agricultural Systems Univ. of Southern Queensland Toowoomba QLD 4300 Australia

Publisher

Wiley

Subject

Agronomy and Crop Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/agj2.21223

Reference76 articles.

1. AN AUTOMATED SAMPLING SYSTEM FOR MEASURING SOIL pH

Cited by 3 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. From Lab to Field: Advancements and Applications of On-The-Go Soil Sensors for Real-Time Monitoring;Eurasian Soil Science;2024-07-30

2. Comparative Analysis of Remote Sensing via Drone and On-the-Go Soil Sensing via Veris U3: A Dynamic Approach;ECRS 2023;2023-12-14

3. Integrating Remote Sensing, Proximal Sensing, and Probabilistic Modeling to Support Agricultural Project Planning and Decision-Making for Waterlogged Fields;Water;2023-03-29

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