| Agricultural engineering |
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| Hyperspectral imaging for early detection of soybean mosaic disease based on convolutional neural network model |
Jiangsheng GUI1,2( ),Zixian WU1,Kai LI2 |
1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2. College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China |
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Abstract In order to reduce the impact of mosaic disease on soybean production and explore a theoretical basis for rapid detection of early soybean mosaic disease, a novel hyperspectral detection method for early soybean mosaic disease based on convolutional neural network (CNN) model was proposed. First, soybean samples inoculated separately with SC3, SC7 viruses and normal soybean samples (Nannong 1138-2) were collected through a hyperspectral system. A region of 40 pixel×40 pixel was selected as the region of interest (ROI) and the average spectral information of ROI was extracted. Then, the CNN model was established based the hyperspectral image. Finally, the recognition rate of the training set in the CNN model reached 94.79%, and the recognition rate of the prediction set reached 92.08%. The recognition rate of the mosaic leaf inoculated with SC3 virus was 88.75%, and the recognition rate of the mosaic leaf inoculated with SC7 virus was 93.13%, and the recognition rate of the normal leaf was 94.38%. Compared with the least square-support vector machine (LS-SVM) and extreme learning machine (ELM) models, the CNN model can more fully extract the deep features of the spectrum, and the extracting effect was significantly improved. Thus, this research shows that the CNN model can achieve the detection of early soybean mosaic disease more accurately, and combining the CNN model with hyperspectral methods also provides a new idea for plant disease detection.
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Received: 15 May 2018
Published: 25 April 2019
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Corresponding Authors:
Jiangsheng GUI
E-mail: jsgui@zstu.edu.cn
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基于卷积神经网络模型的大豆花叶病初期高光谱检测
为减轻花叶病对大豆产量的影响，实现对大豆花叶病害初期的快速检测，本文提出了一种基于卷积神经网络（convolutional neural network, CNN）模型的大豆花叶病害的诊断识别方法。首先对分别接种SC3、SC7病毒7 d后发病初期及正常的‘南农1138-2’大豆样本各80片（共计240片）进行高光谱图像采集，根据其图像信息提取并计算感兴趣区域的平均光谱值，建立基于高光谱图像的CNN模型。最终模型训练集识别率达到94.79%，预测集识别率达到92.08%，其中对接种SC3病毒的花叶病叶片识别率为88.75%，对接种SC7病毒的花叶病叶片识别率为93.13%，对正常叶片识别率为94.38%。对比最小二乘支持向量机和极限学习机模型，CNN模型能够更充分提取光谱的深层特征信息，识别效果显著提高。研究表明，基于高光谱图像的CNN模型能够更精确地实现对大豆花叶病初期检测，将CNN与高光谱结合的方法也为病害检测提供了一种新思路。
关键词：
大豆,
花叶病,
高光谱检测,
卷积神经网络
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