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UAVベースの多層機能選択は,乾燥地域の綿の窒素含有量の推定を改善します.

Fengxiu Li1,2, Chongqi Zhao1,2, Yingjie Ma1,2

  • 1College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.

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まとめ

綿の窒素状態の正確な推定は,UAV画像を使用して,主要なリモートセンシング機能を選択することによって改善されます. ランダムな森林モデルにより 乾燥地域における精密な窒素管理が実現しました

キーワード:
ボルータSHAPエラスティック・ネットコットン機械学習マルチスペクトル画像窒素

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科学分野:

  • 農業科学
  • リモートセンシング
  • データサイエンス

背景:

  • 綿の収穫と繊維の品質には 窒素が不可欠です
  • 綿植物の窒素濃度 (PNC) をUAV画像から推定することは,複雑で冗長なリモートセンシングデータのために困難です.
  • これはモデル精度と 精度の高い農業の 移転性を制限します

研究 の 目的:

  • 綿のPNCをUAVの遠隔検知データを用いて推定するための正確で転送可能な方法を開発する.
  • 最適な機械学習アルゴリズムとPNC推定のための重要なスペクトル特性を特定する.
  • 綿の生産における精密な窒素管理のためのガイドラインを提供すること.

主な方法:

  • データの次元性を減らすために,Elastic NetとBoruta-SHAPを組み合わせた階層的な特徴選択スキームが採用されました.
  • 6つの機械学習アルゴリズムは,綿のPNCを推定する際の性能について評価されました.
  • モデル出力を検証し,窒素ダイナミクスを評価するために,フィールド観察を使用した.

主要な成果:

  • 5つの重要なリモートセンシング機能 (Mean_B,Mean_R,NDRE_GOSAVI,NDVI,GRVI) は,モデルの性能を大幅に改善しました.
  • ランダムフォレストアルゴリズムは,R2値0.97-0.98とRMSE値0.05-0.08で優れたパフォーマンスを示しました.
  • 綿のPNCは開発過程で減少し,最適の灌と窒素肥料はより高いレベルを維持しました.

結論:

  • この研究は,UAV画像から綿のPNCの推定の正確性と移転性を向上させました.
  • この発見は,精密な窒素管理のためにランダムな森林と選択されたスペクトル指標の使用を支持する.
  • この研究は,データ主導の窒素戦略を通じて,乾燥した環境での綿の生産を最適化するための実用的なガイドラインを提供します.