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Updated: Jun 27, 2026

Remote Sensing Evaluation of Two-spotted Spider Mite Damage on Greenhouse Cotton
05:03

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多源遠隔感知データに基づくたばこ葉病の分類

Ke Chen1, Jian Guo2, Linyi Liu1

  • 1State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.

Frontiers in plant science
|February 20, 2026
PubMed
まとめ

この研究では,複数のソースのデータを用いた強力なたばこ葉病の分類方法が導入され, 88.7%の精度を達成しました. このアプローチは,病気の評価を改善するために,ハイパースペクトルデータ,葉面指数,およびクロロフィール含有量を組み合わせています.

キーワード:
連続波束変換である.超スペクトル反射率データランダムな森 ランダムな森リモートセンシング (遠隔感知)タバコの葉のたばこ

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Light Acquisition02:16

Light Acquisition

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