Chinese Journal OF Rice Science >
Influence of Image Features and Sample Sizes on Rice Pest Identification
Received date: 2017-09-21
Revised date: 2017-12-17
Online published: 2018-07-10
【Objective】In the traditional pattern recognition methods, image features and the sizes of training samples have a great influence on the identification results of target objects from a large number of distraction objects. Our objective is to study the influence of different image features and sample sizes on identification of rice light-trapped pests. 【Methods】 Rice light-trapped insects were divided into two broad categories：big insects and small insects. The global and local image features of all insects were extracted and different sizes of training samples were set to train support vector machine classifiers. 【Result】The support vector machine classifier based on the combination of global features and HOG features could obtain the identification rate of 91.4% and false detection rate of 8.6% when the non-target sample size was fourfold as many as target samples in big rice pests. The support vector machine classifier based on global features could obtain the identification rate of 94.9% and false detection rate of 4.9% when the non-target sample size was two times as many as target samples in small rice pests. 【Conclusion】In the small sample sets, appropriate image features and reasonable training sample proportion help achieve good identification results when some targets need to be identified from a large number of non-target objects.
Pengpeng MA, Aiming ZHOU, Qing YAO, Baojun YANG, Jian TANG, Xiuqiang PAN . Influence of Image Features and Sample Sizes on Rice Pest Identification[J]. Chinese Journal OF Rice Science, 2018 , 32(4) : 405 -414 . DOI: 10.16819/j.1001-7216.2018.7116
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