TCSRNet: a lightweight tobacco leaf curing stage recognition network model.

Abstract

Due to the constraints of the tobacco leaf curing environment and computational resources, current image classification models struggle to balance recognition accuracy and computational efficiency, making practical deployment challenging. To address this issue, this study proposes the development of a lightweight classification network model for recognizing tobacco leaf curing stages (TCSRNet). Firstly, the model utilizes an Inception structure with parallel convolutional branches to capture features at different receptive fields, thereby better adapting to the appearance variations of tobacco leaves at different curing stages. Secondly, the incorporation of Ghost modules significantly reduces the model's computational complexity and parameter count through parameter sharing, enabling efficient recognition of tobacco leaf curing stages. Lastly, the design of the Multi-scale Adaptive Attention Module (MAAM) enhances the model's perception of key visual information in images, emphasizing distinctive features such as leaf texture and color, which further improves the model's accuracy and robustness. On the constructed tobacco leaf curing stage dataset (with color images sized 224×224 pixels), TCSRNet achieves a classification accuracy of 90.35% with 158.136 MFLOPs and 1.749M parameters. Compared to models such as ResNet34, GhostNet, ShuffleNetV2×1.5, EfficientNet-b0, MobileViT-xs, MobileNetV2, MobileNetV3-large, and MobileNetV3-small, TCSRNet demonstrates superior performance in terms of accuracy, FLOPs, and parameter count. Furthermore, when evaluated on the public V2 Plant Seedlings dataset, TCSRNet maintains an impressive accuracy of 97.15% compared to other advanced network models. This research advances the development of lightweight models for recognizing tobacco leaf curing stages, providing theoretical support for smart tobacco curing technologies and injecting new momentum into the digital transformation of the tobacco industry.

Plant phenotyping relevance

タバコ葉の画像から硬化段階という植物状態を認識する軽量画像分類モデルを開発し、専用データセットおよび公開データセットで性能比較・評価しているため、フェノタイピング手法開発が中心である。

Code and data availability

The paper describes a self-collected tobacco leaf curing stage image dataset (48,164 augmented training images) and the TCSRNet model, but no blocks contain any public deposit, availability statement, or URL for the dataset, images, code, or trained model. The only public dataset mentioned (V2 Plant Seedlings) is a pre

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