An ensemble multi-dimensional randomization network for intelligent recognition of tobacco baking stage.

Abstract

In recent years, image processing technology has been increasingly studied on intelligent unmanned platforms, and the differences in the shooting environment during tobacco baking pose challenges to image processing algorithms. To address this problem, an ensemble multi-dimensional randomization network (EMRNet) for intelligent recognition of tobacco baking stage is proposed. The first is to obtain the tobacco leaf area during the baking process. Then, a multi-dimensional randomization network (MRNet) is designed to recognize tobacco baking stage. The effectiveness of MRNet lies in multi-scale hidden layer feature extraction, which can effectively enhance the expression ability of features to overcome the impact of differences between different environments on the tobacco baking stage. Finally, MRNet is used as component learner for constructing an ensemble randomization network structure to distinguish the tobacco baking stage. On the constructed tobacco baking stage dataset, EMRNet achieves 89.14% accuracy with 642.96MFLOPs. Compared with SVM, MLP, BP, ELM, CRVFL and other algorithms, EMRNet shows excellent performance in accuracy and model complexity. The proposed method explores the application of image processing technology in crop baking and drying, providing theoretical support for intelligent baking technology.

Plant phenotyping relevance

タバコ葉の面積取得と画像処理による乾燥・ベーキング段階認識を中心に、環境差に対する認識手法を開発・評価しており、葉の状態を推定する植物フェノタイピング手法に該当する。

Code and data availability

The article describes a self-collected tobacco baking stage image dataset (Shaanxi, Sichuan, Chongqing, 2023) and the EMRNet model, but no blocks contain any data availability statement, public dataset deposit, code repository, or trained model release. No authors' public URL for assets is provided; only the article's

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