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TOBACCO LEAF GRADING BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS AND MACHINE VISION

文献类型： 外文期刊

作者： Lu, Mengyao 1 ; Jiang, Shuwen 1 ; Wang, Cong 1 ; Chen, Dong 1 ; Chen, Tian'en 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Nongxin Nanjing Smart Agr Res Inst, Nanjing, Peoples R China

关键词： Convolutional neural network; Deep learning; Image classification; Transfer learning; Tobacco leaf grading

期刊名称：TRANSACTIONS OF THE ASABE （ 影响因子：1.238； 五年影响因子：1.39 ）

ISSN： 2151-0032

年卷期： 2022 年 65 卷 1 期

页码：

收录情况： SCI

摘要： Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products.

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