TRD-Net:選択的収穫のための改善されたYOLO v8に基づく効率的なトマトの熟成度検出ネットワーク
Xiangpeng Fan1,2, Xiujuan Chai1,2
1Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China.
Frontiers in plant science
|February 13, 2026
まとめ
新しい軽量トマトの成熟度検出ネットワーク (TRD-Net) は,複雑な環境におけるリアルタイム認識と成熟度検出を改善します. このモデルは,選択的収穫ロボットの高速化とより低いコンピューティング要求を提供します.
科学分野:
- 農業工学 農業工学とは
- コンピュータビジョン コンピュータビジョン
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
背景:
- 選択的収穫には,果実の正確な認識と成熟度検出が必要です.
- 既存の方法は,閉塞や照明の変動がある複雑な環境で課題に直面しています.
研究 の 目的:
- リアルな環境でのパフォーマンスを向上させるため,新しい軽量トマトの熟成度検出ネットワーク (TRD-Net) を開発する.
- トマトのリアルタイム認識と成熟度検出を自動収穫システムで強化する.
主な方法:
- 3,330枚のリアルな画像からなるトマトのデータセットが作成されました.
- YOLO v8s ネットワークは,空間とチャネル再構築コンボリューション (SCRConv) とSimAM attention.を用いることで改善されました.
- MPDIoUの損失関数は,CIoUの損失を代替して,検出を向上させました.
主要な成果:
- TRD-Netは0.9581のmAP@0.5を達成し,4.32%の改善となりました.
- モデルのサイズは19.69%減少し,推論時間は画像あたり8.7msでした.
- パラメータとFLOPはそれぞれ19.69%と22.03%減少した.
結論:
- TRD-Netはトマトの認識と成熟度検出の正確さと効率の有意な改善を示しています.
- このモデルは,複雑なガーデニング環境におけるリアルタイムアプリケーションに適しています.
- この研究は,選択的収穫ロボットのための機械ビジョンシステムの開発をサポートします.
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