Rhizocotonia Solani Recognition Algorithm Based on Convolutional Neural Network

doi:10.16819/j.1001-7216.2019.8051

Abstract

Abstract:

【Objective】Rice sheath blight is one of the three major diseases in rice production.The convolutional neural network which stands out for automatic identification of rice shealth blight can compensate for the lack of human identification. To solve this problem and prevent diseases deterioration, accurate identification of diseases types is of great significance.【Method】The convolutional neural network method was used to recognize rice sheath blight and compared with the recognition method based on support vector machine.【Result】The convolutional neural network method showed the recognition rate of 97%, better than that of support vector machine(95%).【Conclusion】The application of convolutional neural network to the identification of rice sheath blight is feasible and makes up for the lack of artificial recognition. The model trained by this algorithm has great recognition performance.

Key words: Rhizocotonia solani, convolutional neural network, classification and recognition

摘要：

【目的】水稻纹枯病是影响水稻生产的三大病害之一。研究卷积神经网络对水稻纹枯病的自动识别,弥补人工识别的不足,对预防和准确识别水稻蚊枯病类型有着重要意义。【方法】以卷积神经网络进行水稻纹枯病识别,并与基于支持向量机的识别方法进行对比。【结果】卷积神经网络识别率达到97%,优于支持向量机的95%。【结论】卷积神经网络运用于水稻纹枯病识别是可行的,弥补了人工识别的不足。此算法训练的模型有着较好的识别性能。

关键词: 水稻纹枯病, 卷积神经网络, 分类识别

CLC Number:

Tingting LIU, Ting WANG, Lin HU. Rhizocotonia Solani Recognition Algorithm Based on Convolutional Neural Network[J]. Chinese Journal OF Rice Science, 2019, 33(1): 90-94.

刘婷婷, 王婷, 胡林. 基于卷积神经网络的水稻纹枯病图像识别[J]. 中国水稻科学, 2019, 33(1): 90-94.

Figures/Tables 3

References 41

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样本正确识别率 Correct recognition rate of samples	第一次 First	第二次 Second	第三次 Third	第四次 Fourth	第五次 Fifth
卷积神经网络CNN	100.0	95.0	92.5	97.5	100.0
支持向量机SVM	97.5	92.5	90.0	95.0	100.0

样本正确识别率 Correct recognition rate of samples	第一次 First	第二次 Second	第三次 Third	第四次 Fourth	第五次 Fifth
卷积神经网络CNN	100.0	95.0	92.5	97.5	100.0
支持向量机SVM	97.5	92.5	90.0	95.0	100.0

识别算法 Recognition algorithm	观测数 Observation number	求和 Sum	平均 Average	方差 Variance
行1：卷积神经网络Row1:CNN	5	4.850	0.9700	0.0011
行2：支持向量机Row2:SVM	5	4.750	0.9500	0.0016
列1：第一次Column1:First	2	1.975	0.9875	0.0003
列2：第二次Column2:Second	2	1.875	0.9375	0.0003
列3：第三次Column3:Third	2	1.825	0.9125	0.0003
列4：第四次Column4:Fourth	2	1.925	0.9625	0.0003
列5：第五次Column5:Fifth	2	2.000	1.0000	0.0003

识别算法 Recognition algorithm	观测数 Observation number	求和 Sum	平均 Average	方差 Variance
行1：卷积神经网络Row1:CNN	5	4.850	0.9700	0.0011
行2：支持向量机Row2:SVM	5	4.750	0.9500	0.0016
列1：第一次Column1:First	2	1.975	0.9875	0.0003
列2：第二次Column2:Second	2	1.875	0.9375	0.0003
列3：第三次Column3:Third	2	1.825	0.9125	0.0003
列4：第四次Column4:Fourth	2	1.925	0.9625	0.0003
列5：第五次Column5:Fifth	2	2.000	1.0000	0.0003