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Mykhailo Lohachov, Ryoji Korei, Kazuo Oki, Koshi Yoshida, Issaku Azechi, Salem Ibrahim Salem and Nobuyuki Utsumi
This article investigates approaches for broccoli harvest time prediction through the application of various machine learning models. This study?s experiment is conducted on a commercial farm in Ecuador, and it integrates in situ weather and broccoli gro...
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Lihong Wang, Tianxiao Li, Hui Liu, Zuowei Zhang, Aizheng Yang and Hongyu Li
Global climate warming and increased climate variability may increase the number of annual freeze?thaw cycles (FTCs) in temperate zones. The occurrence of more frequent FTCs is predicted to influence soil carbon and nitrogen cycles and increase nitrogen ...
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Zuxuan Song, Fangmei Liu, Wenbo Lv and Jianwu Yan
Exploring the transformation process of urban agricultural functions and its interaction with carbon effects based on regional differences is of great positive significance for achieving a low-carbon sustainable development of agriculture in metropolitan...
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Dominika Sieracka, Maciej Zaborowicz and Jakub Frankowski
Currently, there is a significant increase in interest in hemp cultivation and hemp products around the world. The hemp industry is a strongly developing branch of the economies of many countries. Short-term forecasting of the hemp seed and grain yield w...
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Srinivasagan N. Subhashree, C. Igathinathane, Adnan Akyuz, Md. Borhan, John Hendrickson, David Archer, Mark Liebig, David Toledo, Kevin Sedivec, Scott Kronberg and Jonathan Halvorson
Farmers and ranchers depend on annual forage production for grassland livestock enterprises. Many regression and machine learning (ML) prediction models have been developed to understand the seasonal variability in grass and forage production, improve ma...
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