Abstract
Chemical control is the major approach to handle the American Hyphantria cunea issue; however, it often causes chemical pollution and resource waste. How to precisely apply pesticide to reduce pollution and waste has been a difficult problem. The premise of accurate spraying of chemicals is to accurately determine the location of the spray target. In this paper, an algorithm based on a convolutional neural network (CNN) is proposed to locate the screen of American Hyphantria cunea. Specifically, comparing the effect of multicolor space-grouping convolution with that of the same color space-grouping convolution, the better effect of different color space-grouping convolution is first proved. Then, RGB and YIQ are employed to identify American Hyphantria cunea screen. Moreover, a noncoincident sliding window method is proposed to divide the image into multiple candidate boxes to reduce the number of convolutions. That is, the probability of American Hyphantria cunea is determined by grouping convolution in each candidate box, and two thresholds (E and Q) are set. When the probability is higher than E, the candidate box is regarded as excellent; when the probability is lower than Q, the candidate box is regarded as unqualified; when the probability is in between, the candidate box is regarded as qualified. The unqualified candidate box is eliminated, and the qualified candidate box cannot exit the above steps until the number of extractions of the candidate box reaches the set value or there is no qualified candidate box. Finally, all the excellent candidate boxes are fused to obtain the final recognition result. Experiments show that the recognition rate of this method is higher than 96%, and the processing time of a single picture is less than 150 ms.
Highlights
As a world quarantine pest, American Hyphantria cunea seriously damages trees, fruit trees, and crops
Based on the survey results, adult American Hyphantria cunea is detected in a number of nonepidemic areas in 2018, and the diffusion situation is grim. e epidemic may spread in Changzhou, Jiangsu province, and Huanggang, Hubei province
Excellent candidate box reservation value of N is zero. e image is divided into several candidate frames by the sliding window method. e convolutional neural network (CNN) model of RGB and YIQ is used to score each candidate frame and classify it into three levels based on the score. e excellent candidate frame is retained, and the unqualified candidate frame is eliminated. e region in the qualified candidate box is extracted and screened again, and the width and height of the window before each extraction candidate box are reduced to half of the original size. e extraction and screening process are completed when the number of candidate box extraction reaches the set value or there is no qualified candidate box
Summary
As a world quarantine pest, American Hyphantria cunea seriously damages trees, fruit trees, and crops. E target application technology based on machine vision can effectively improve the spraying efficiency, reduce the dosage, and avoid chemical pollution. Shape, and distribution of the screen of American Hyphantria cunea larvae, a new screen location algorithm is proposed based on the convolutional neural network. E screen data set of American Hyphantria cunea larvae is first created based on the color, texture, other characteristics, and distribution. En, a multicolor space-based CNN architecture is proposed, and the screen data set of American Hyphantria cunea larvae is employed to train the model. E screen of American Hyphantria cunea larvae is located by the sliding window method and CNN. All the excellent candidate frames are fused, and the original image is painted with the outline of the screen of American Hyphantria cunea larvae
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