Zhao Panzhen, Wang Songfeng, Hao Xianwei, Wang Zhisheng, Zou Jun, Ren Jie, Dai Yingpeng
Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, 266101, China.
Graduate School of Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Sci Rep. 2025 Jan 8;15(1):1346. doi: 10.1038/s41598-024-84895-y.
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.
近年来，图像处理技术在智能无人平台上的研究日益增多，而烟草烘烤过程中拍摄环境的差异给图像处理算法带来了挑战。为解决这一问题，提出了一种用于烟草烘烤阶段智能识别的集成多维随机化网络（EMRNet）。首先是获取烘烤过程中的烟叶面积。然后，设计了一个多维随机化网络（MRNet）来识别烟草烘烤阶段。MRNet的有效性在于多尺度隐藏层特征提取，它可以有效增强特征的表达能力，以克服不同环境差异对烟草烘烤阶段的影响。最后，将MRNet用作组件学习器来构建集成随机化网络结构，以区分烟草烘烤阶段。在构建的烟草烘烤阶段数据集上，EMRNet以642.96MFLOPs的计算量实现了89.14%的准确率。与支持向量机（SVM）、多层感知器（MLP）、反向传播（BP）、极限学习机（ELM）、压缩随机向量函数链接网络（CRVFL）等算法相比，EMRNet在准确率和模型复杂度方面表现优异。该方法探索了图像处理技术在作物烘烤干燥中的应用，为智能烘烤技术提供了理论支持。
