Maturity discrimination of tobacco leaves for tobacco harvesting robots based on a Multi-Scale branch attention neural network

文献类型： 外文期刊

第一作者： Dai, Yingpeng

作者： Dai, Yingpeng;Zhao, Panzhen;Wang, Yutan;Zhao, Panzhen

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关键词： Maturity discrimination; Tobacco harvesting robots; Neural network; Branch attention mechanism

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 224 卷

页码：

收录情况： SCI

摘要： Traditional machine vision methods are susceptible to environmental influences, low recognition accuracy, and poor real-time performance in identifying tobacco leaf maturity. Currently, the maturity of tobacco leaves heavily relies on manual observation, and existing research on tobacco maturity discrimination focuses on standard light sources and standard background, which difficult to be applied to tobacco harvesting robots in natural environment. To tackle this problem, a maturity discrimination method based on the branch attention mechanism is proposed. Firstly, the region of interest in the extracted tobacco leaf images is obtained as a foundation for subsequent image processing. Then, a deep learning network model called the Multi-Scale Branch Attention Network (MSBANet) is designed for identification of tobacco leaf maturity. The effectiveness of MSBANet stems from its newly designed modules for multi-scale information extraction and branch attention. These modules help overcome the impact of lighting conditions and enable the extraction of more comprehensive and informative feature representations, capturing the relationship between phenotype and maturity. On the selfconstructed dataset of tobacco leaf maturity (color images are 576 x 768 pixels), MSBANet-L achieves 89.6 % accuracy with 7.1GFLOPs and MSBANet-S achieves 88.2 % accuracy with 1.7 GFLOPs. When compared with widely used BP and MobileNet, as well as state-of-the-art structures such as YOLOv7-tiny-SiLU and FasterNet-T0, MSBANet demonstrates excellent performance. This method preliminarily explores the maturity discrimination of tobacco leaves in actual environments and shows a potential for development of tobacco harvesting robots.

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