Moisture content monitoring of cigar leaves during drying based on a Convolutional Neural Network

IF 2 4区 农林科学 Q2 AGRONOMY International Agrophysics Pub Date : 2023-06-24 DOI:10.31545/intagr/165775
Yang Hao, Zhang Tong, Yang Wei Li, Xiang Huan, Liu Xiao Li, Zhang Qi, Liu Lei, Y. You, Liu Ya Jie, Guo Shi Ping, Zeng Shu Hua
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Abstract

. The moisture content of cigar leaves during drying is an important indicator for controlling the management of drying rooms. At present, the determination of cigar leaf moisture content is mainly dependent on traditional destructive detection methods, which are inefficient and damaging to plants. In this study, a Convolution Neural Network method consisting of digital images for monitoring the moisture content of cigar leaves during the drying process was proposed. In this study, the Convolution Neural Network model was trained to learn the relationship between the images and the corresponding moisture content using the extracted colour, shape, and texture features as input factors. In order to compare the Convolution Neural Network estimation results, a widely used traditional machine learning algorithm was applied. The results demonstrated that the estimated value of Convolution Neural Network agreed with the predicted value; the R 2 was 0.9044, and the average accuracy was 87.34%. These results were better than those produced by traditional machine learning methods. The generalization test of the proposed method was conducted using varieties of cigar leaves in other drying rooms. The results showed that Convolution Neural Network is a viable method for an accurate estimation of the moisture content, the R 2 was 0.8673 and the average accuracy was 86.81%. The Convolution Neural Network established by the features extracted from digital images could accurately estimate the moisture content of cigar leaves during drying and was therefore shown to be an effective monitoring tool.
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基于卷积神经网络的雪茄干燥过程水分监测
。干燥过程中雪茄叶的含水量是控制干燥室管理的重要指标。目前,雪茄叶水分含量的测定主要依赖于传统的破坏性检测方法,效率低,对植物有害。在本研究中,提出了一种由数字图像组成的卷积神经网络方法,用于监测雪茄叶在干燥过程中的水分含量。在这项研究中,使用提取的颜色、形状和纹理特征作为输入因素,训练卷积神经网络模型来学习图像与相应水分含量之间的关系。为了比较卷积神经网络的估计结果,应用了一种广泛使用的传统机器学习算法。结果表明,卷积神经网络的估计值与预测值一致;R2为0.9044,平均准确率为87.34%。这些结果优于传统的机器学习方法。在其他干燥室对不同品种的雪茄叶进行了推广试验。结果表明,卷积神经网络是一种准确估计水分含量的可行方法,R2为0.8673,平均准确率为86.81%。利用从数字图像中提取的特征建立的卷积神经网络可以准确地估计雪茄叶在干燥过程中的水分含量,是一种有效的监测工具。
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来源期刊
International Agrophysics
International Agrophysics 农林科学-农艺学
CiteScore
3.60
自引率
9.10%
发文量
27
审稿时长
3 months
期刊介绍: The journal is focused on the soil-plant-atmosphere system. The journal publishes original research and review papers on any subject regarding soil, plant and atmosphere and the interface in between. Manuscripts on postharvest processing and quality of crops are also welcomed. Particularly the journal is focused on the following areas: implications of agricultural land use, soil management and climate change on production of biomass and renewable energy, soil structure, cycling of carbon, water, heat and nutrients, biota, greenhouse gases and environment, soil-plant-atmosphere continuum and ways of its regulation to increase efficiency of water, energy and chemicals in agriculture, postharvest management and processing of agricultural and horticultural products in relation to food quality and safety, mathematical modeling of physical processes affecting environment quality, plant production and postharvest processing, advances in sensors and communication devices to measure and collect information about physical conditions in agricultural and natural environments. Papers accepted in the International Agrophysics should reveal substantial novelty and include thoughtful physical, biological and chemical interpretation and accurate description of the methods used. All manuscripts are initially checked on topic suitability and linguistic quality.
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