Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging
Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging
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DOI：
10.3390/rs12152348
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发表时间：
2020-08-01
期刊：
影响因子：
5
通讯作者：
Lien, Chou-Tien
中科院分区：
文献类型：
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作者：
Chen, Shih-Yu;Chang, Chuan-Yu;Lien, Chou-Tien
有缺陷的咖啡豆分为黑豆、发酵豆、发霉豆、虫蛀豆、羊皮豆和碎豆，虫蛀豆是最常见的类型。在过去，咖啡豆是人工筛选的，眼睛疲劳会导致错误识别。采用推扫式可见-近红外（VIS-NIR）高光谱传感器获取咖啡豆图像，并在此基础上提出了一种高光谱虫害检测算法（HIDDA）。首先，通过利用约束能量最小化（CEM）开发的频带选择方法，使用约束能量最小化-约束频带相关性最小化（CEM-BDM）、最小方差频带优先（MinV-BP）、基于最大方差的bp（MaxV-BP）、顺序前向CTBS（SF-CTBS）、顺序后向CTBS（SB-CTBS）和主成分分析（PCA）来选择频带，并进一步提出了两种分类器方法。一个将CEM与支持向量机（SVM）相结合进行分类，而另一个则使用卷积神经网络（CNN）和深度学习进行分类，然后分析了六种波段选择方法。实验采集了1139颗豆子和20幅图像，结果表明，只需要三个波段就可以达到95%的准确率和90%的kappa系数。这些研究结果表明，850-950 nm是准确识别虫害豆的重要波长范围，HIDDA确实可以仅用一个光谱特征检测虫害豆，这将在未来的实际应用和商业化过程中提供优势。
The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850-950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future.