Study On Identification Of Pests Based On Machine Vision

Precise identification of pests is a requisite for integrated pest management (IPM). The traditional method of pest identification is mainly achieved by limited plant protection personnels and entomologists, which is laborious and time-consuming. Machine vision can solve the problems of labor shortage and low accuracy in artificial identification, which has high research value. Compared to other machine vision application, pest identification is more difficult due to small size of pests and wider varieties. In this context, identification of agricultural typical pests was studied, based on feature extraction, convolutional neural network (CNN) and hyperspectral imaging. Besides, pest image indetificaition systems were developed. The main contents of the thesis are as follows:(1) The pest identification method based on image feature extraction was proposed, with a route of "image segmentation--feature extraction--classifier design". In the phase of image segmentaion, a cornor detection algorithm was applied for image cropping to reduce the background area, which could improve the ability of Otsu segmentation method for finding the optimal threshold under the condition of uneven ambient light or smaller object. Based on the segmented pest target, global features were extracted, including color, morphological and texture characteristics. Besides, the SURF algorithm was explored to extract local features, which was invariant to image scaling, translation and rotation. The SVM classifier was used to identify 9 kinds of pests. The accuracy using global features achieved 85.9% and the accuracy using local features was 77.4%.(2) In order to realize recognition of pest images under natural backgrounds, a method based on CNN was proposed. Considering the traits of the pest image set, a CNN model of 12 layers was constructed. On the basis of the basic structure of CNN, normalization layer was added to acquire better generalization and Relu was applied as excite function. Other hidden layers were also adjusted according to the development platform. The CNN model used RGB images of 128*128 as input, after mapping transform of hidden layer, and then calculated the corresponding class values. When training epoch reached 45, the recognition rates of test set was 76.7%. Besides, the effect of image pre-segmentation on the CNN model was explored. The result showed that the GrabCut segmentation method could reduce the recogntion difficulty of the original pest image set, leading to higher model accuracy.(3) Hyperspctral imaging technolodgy was applied for detection and recogntion of pests. Two objectives were studied using NIR wavelength(1000nm-1600nm)。 The first objective was detection of Pieris rapae larvae:The developed SPA-PLS-DA model, combined with image process algorithm, was used for pixel-wise detection of larvae in the hyperspectral images of mixed samples; a binary image was produced to display the location and approximate shape of larvae precisely and intuitively. The second objective was recognition of pests:hyperspectral data of Cnaphalocrocis medinalis Guenee, Chilo suppressalis, Diaphania perspectalis and Ostrinia nubilalis were acquired; different preprocessing methods (Raw, SG, SNV and MSC) were implemented, and the result showed that the Raw was the best. Based on the 9 EWs selected by SPA,4 models of PLS-DA, BPNN, ELM, SVM were developed; by comparison, the SPA-ELM model achieved the best prediction result, with 100% classification accuracy for both the calibration set and the prediction set.(4) Pest identification systems were designed. An industrial camera was used to set up an indoor image acquisition platform. And a webcam was used for remote acquisition and transmission of pest images. Pest identification software based on image feature extraction was developed, which had functionalities of image segmentation, feature extraction. This software could identify the pest image acquired from local disk and usb camera. Pest identification software based on CNN was developd, which realized training and recognition of CNN model. This software had two training modes, and used saved model to indentify pest images.