Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems.-猫眼课题宝

DOI：

10.1186/s13007-018-0349-9

发表时间：

2018

期刊：

Plant methods

影响因子：

5.1

通讯作者：

中科院分区：

生物学2区

文献类型：

作者：

Nagasubramanian K;Jones S;Sarkar S;Singh AK;Singh A;Ganapathysubramanian B

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169113/

炭腐病是一种真菌病害，在温暖干燥的条件下茁壮成长，影响全球大豆和其他重要农作物的产量。需要对疾病症状进行稳健、自动和一致的早期检测和量化，这在用于开发改良品种的育种计划和用于实施病害控制措施以保护产量的作物生产中是重要的。目前的植物疾病表型分析方法主要是视觉的，因此是缓慢的，易于人为错误和变化。人们对高光谱成像应用于疾病症状的早期检测越来越感兴趣。然而，高光谱数据的高维性使得建立有效的分析管道来识别疾病变得非常重要，这样就可以做出有效的作物管理决策。这项工作的重点是确定最有效的高光谱波段，可以区分健康和患病的大豆茎标本在生长季节的早期适当的疾病管理的最小数量。在接种后3、6、9、12和15天捕获了代表健康和感染茎的111个高光谱数据立方体。我们使用来自4种不同基因型的接种标本和对照标本。每张高光谱图像都是在383-1032 nm范围内的240种不同波长下拍摄的。我们将从240个波段中识别最佳波段组合的问题转化为一个优化问题。我们使用遗传算法作为优化器和支持向量机作为分类器的组合，用于识别最有效的波段组合。健康和感染的大豆茎样品之间的二进制分类使用所选择的六个波段组合（475.56，548.91，652.14，516.31，720.05，915.64 nm）获得了97%的分类精度为感染类。此外，我们取得了90.91%的分类准确率为测试样品接种后3天，使用选定的六个波段组合。结果表明，这些精心选择的波段比单独的RGB图像信息量更大，能够早期识别大豆炭腐病感染。所选波段可用于多光谱相机对大豆炭腐病的遥感识别。

Charcoal rot is a fungal disease that thrives in warm dry conditions and affects the yield of soybeans and other important agronomic crops worldwide. There is a need for robust, automatic and consistent early detection and quantification of disease symptoms which are important in breeding programs for the development of improved cultivars and in crop production for the implementation of disease control measures for yield protection. Current methods of plant disease phenotyping are predominantly visual and hence are slow and prone to human error and variation. There has been increasing interest in hyperspectral imaging applications for early detection of disease symptoms. However, the high dimensionality of hyperspectral data makes it very important to have an efficient analysis pipeline in place for the identification of disease so that effective crop management decisions can be made. The focus of this work is to determine the minimal number of most effective hyperspectral wavebands that can distinguish between healthy and diseased soybean stem specimens early on in the growing season for proper management of the disease. 111 hyperspectral data cubes representing healthy and infected stems were captured at 3, 6, 9, 12, and 15 days after inoculation. We utilized inoculated and control specimens from 4 different genotypes. Each hyperspectral image was captured at 240 different wavelengths in the range of 383–1032 nm. We formulated the identification of best waveband combination from 240 wavebands as an optimization problem. We used a combination of genetic algorithm as an optimizer and support vector machines as a classifier for the identification of maximally-effective waveband combination. A binary classification between healthy and infected soybean stem samples using the selected six waveband combination (475.56, 548.91, 652.14, 516.31, 720.05, 915.64 nm) obtained a classification accuracy of 97% for the infected class. Furthermore, we achieved a classification accuracy of 90.91% for test samples from 3 days after inoculation using the selected six waveband combination. The results demonstrated that these carefully-chosen wavebands are more informative than RGB images alone and enable early identification of charcoal rot infection in soybean. The selected wavebands could be used in a multispectral camera for remote identification of charcoal rot infection in soybean.