131. Predicting plant-level cabbage yield by assimilating UAV-derived LAI into a crop simulation model-Reference-Cited by-同舟云学术

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131. Predicting plant-level cabbage yield by assimilating UAV-derived LAI into a crop simulation model

Published:2023-07-02 Issue: Volume: Page:
ISSN:
Container-title:Precision agriculture ’23
language:
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Author:

Yokoyama Y.¹,de Wit A.²,Matsui T.³,Tanaka T.S.T.³⁴

Affiliation:

1. Graduate School of Natural Science and Technology, Gifu University, Yanagido, Gifu, Japan.

2. Wageningen Environmental Research, Wageningen, the Netherlands.

3. Faculty of Applied Biological Sciences, Gifu University, Yanagido, Gifu, Japan.

4. AI Advanced Research Center, Gifu University, Yanagido, Gifu, Japan.

Publisher

Wageningen Academic Publishers

Link

https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-947-3_131

Reference11 articles.

1. YOLACT: Real-Time Instance Segmentation

2. Brooks, J. (2019) COCO Annotator. https://github.com/jsbroks/coco-annotator (last accessed on December 21, 2022)

3. 25 years of the WOFOST cropping systems model

4. Predicting within-field soybean yield variability by coupling Sentinel-2 leaf area index with a crop growth model

5. Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability

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1. Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests;Applied Sciences;2024-08-31

2. 解釈可能なAIによる土壌・作物系モデルの開発;Journal of the Robotics Society of Japan;2023

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