| アイテムタイプ |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2024-04-04 |
| 書誌情報 |
en : Computers and Electronics in Agriculture
巻 197,
p. 106911,
発行日 2022
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| タイトル |
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|
タイトル |
Non-destructive Leaf Area Index estimation via guided optical imaging for large scale greenhouse environments |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Tomato |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Deep learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
LAI, greenhouse farming |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Agriculture |
| 資源タイプ |
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|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 著者 |
Baar, Stefan
小林, 洋介
Horie, Tatsuro
佐藤, 和彦
須藤, 秀紹
渡邉, 真也
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| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
This paper presents a financially viable and non-destructive rail-based video monitoring method that utilizes optical image segmentation to estimate the canopy leaf area index (LAI) of greenhouse tomato plants. The LAI is directly related to the time-dependent crop growth and indicates plant health and potential crop yields. A rail-guided mobile camera system was commissioned that records continuous images by scanning multiple rows of two tomato plant species for over two years. UNET semantic image segmentation of the individual image frames was performed to compute the relative leaf area over time. This study also describes the image annotation process necessary to train the neural network and evaluate the segmentation results. The results are calibrated and compared to the defoliation-based (destructive) LAI estimation performed by the grower. This UNET segmentation performs well, which is enabled through the controlled environment and the well-defined boundary conditions provided by the greenhouse environment and the managed measurement conditions. Our results deviate from the manual LAI estimation by less than ten percent. Further, we are able to minimize confusion between foreground and background plants and other obstructions with an estimated error smaller than three percent, which is strictly necessary to produce reproducible results. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Elsevier |
|
言語 |
en |
| DOI |
|
|
関連タイプ |
isVersionOf |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1016/j.compag.2022.106911 |
| ISSN |
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|
収録物識別子タイプ |
PISSN |
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収録物識別子 |
0168-1699 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1872-7107 |
| 権利 |
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|
権利情報 |
© 2022 Elsevier B.V. All rights reserved. |
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言語 |
en |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
cea_197_2022_106911.pdf (21.6 MB)
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