CAJ | 학술논문

利用灯光诱杀稻田害虫,并识别与计数这些害虫是水稻害虫的一种常规但非常重要的测报方法.在灯光诱杀的昆虫中,大多数昆虫是不需要测报的,因此,在人工识别灯诱测报害虫时,需要排除这些昆虫.这种人工识别与计数害虫的方法效率低、任务重、识别准确率差.我们提出了一种基于图像的水稻灯诱害虫自动识别方法.首先,根据测报害虫的形态特征对水稻灯诱昆虫图像进行初步分组,每组昆虫图像中包含一种测报害虫的背面图像、腹面图像和与其形态相似的非测报害虫图像,共3类；然后,提取组内每一张水稻昆虫图像的颜色、形态和纹理共31个特征参数；最后,利用带后验概率的 SVM分类器对每组的3类昆虫图谱进行训练和测试,输出时同一种测报害虫的背面和腹面图像被视为同一种害虫.结果表明,3种较大个体的水稻灯诱测报害虫的平均识别准确率为97．4％.
이용등광유살도전해충,병식별여계수저사해충시수도해충적일충상규단비상중요적측보방법.재등광유살적곤충중,대다수곤충시불수요측보적,인차,재인공식별등유측보해충시,수요배제저사곤충.저충인공식별여계수해충적방법효솔저、임무중、식별준학솔차.아문제출료일충기우도상적수도등유해충자동식별방법.수선,근거측보해충적형태특정대수도등유곤충도상진행초보분조,매조곤충도상중포함일충측보해충적배면도상、복면도상화여기형태상사적비측보해충도상,공3류；연후,제취조내매일장수도곤충도상적안색、형태화문리공31개특정삼수；최후,이용대후험개솔적 SVM분류기대매조적3류곤충도보진행훈련화측시,수출시동일충측보해충적배면화복면도상피시위동일충해충.결과표명,3충교대개체적수도등유측보해충적평균식별준학솔위97．4％.
Automatic identification and count of rice light-trapped pests is a common and important pest forecasting method in paddy fields.However,most of the light-trapped pests are unnecessary to be monitored and must be removed.This manual method is time-consuming and fatiguing with a low accuracy rate.We developed an automatic method for identifying rice light-trapped pests based on the images.Firstly,we divided the images into several groups according to their morphological features.Each group has three classifications:the back-side image of a pest,the abdomen-side image of this pest,and non-forecasting pest image similar to this pest.Then,thirty-one features including the color,shape and texture features were extracted from each insect image.Finally,three support vector machine classifiers with posterior probabilities were used to train and test the three groups of insects,respectively.In the results,the back-side image and the abdomen-side image of a pest were considered as the same species.We achieved a 97.4% accuracy rate in the three species of rice light-trapped pests.