Výsledky vyhledávání - "Seiji SHIMODA"

Akademický článek

Understanding microtopography changes in agricultural landscapes through precision assessments of digital surface models by the UAV-RTK-PPK method without ground control points

Autor: Yasutaka Nakata, Kenta Iwasaki, Seiji Shimoda, Hiroyuki Torita

Publikováno v: Smart Agricultural Technology, Vol 5, Iss , Pp 100269- (2023)

Soil erosion on agricultural land causes severe environmental problems and damages crop plants. Structure-from-motion with multiview stereo (SfM-MVS) together with data obtained via unmanned aerial vehicles (UAVs) helps understand spatiotemporal chan

Externí odkaz: https://doaj.org/article/5316820131ac42cf8746c5a6174a86e1

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Akademický článek

An improved crop calendar model for winter wheat incorporating vernalization and winter survival to project changes in phenology

Autor: Keach Murakami, Toshichika Iizumi, Seiji Shimoda

Publikováno v: Environmental Research: Food Systems, Vol 1, Iss 1, p 011002 (2024)

Crop phenology and associated crop calendars are necessary for designing breeding goals and for developing effective management practices. Winter wheat is a representative biennial, the cultivation schedule of which is constrained by winter climate c

Externí odkaz: https://doaj.org/article/755ab79525fe4a98beff6abc67a11f06

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Akademický článek

Effects of snow compaction ‘yuki-fumi’ on soil frost depth and volunteer potato control in potato–wheat rotation system in Hokkaido

Autor: Seiji Shimoda, Masayuki Onodera, Osamu Okumura, Hideharu Araki, Atsushi Kimura, Kentarou Chiba, Yuko Kusano, Harumitsu Hoshi, Shiho Tamura, Tatsuya Suda, Yasuhiro Kominami, Toshiro Nakatsuji, Tomoyoshi Hirota

Publikováno v: Plant Production Science, Vol 0, Iss 0, Pp 1-12 (2020)

Emergence of unharvested potatoes that survived during winter becomes source for nematodes and diseases, causing serious weed problems in rotational crop fields. Herein, we describe frost killing of unharvested potatoes in potato–wheat rotation fie

Externí odkaz: https://doaj.org/article/5dd30a5f256a489b88b39b4775faa609

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Akademický článek

Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan

Autor: Keach Murakami, Seiji Shimoda, Yasuhiro Kominami, Manabu Nemoto, Satoshi Inoue

Publikováno v: PLoS ONE, Vol 16, Iss 10 (2021)

This study analyzed meteorological constraints on winter wheat yield in the northern Japanese island, Hokkaido, and developed a machine learning model to predict municipality-level yields from meteorological data. Compared to most wheat producing are

Externí odkaz: https://doaj.org/article/ed500f76b2b5484d9c6cc3ee9bdd24f7

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