A Screen Location Method For Treating American Hyphantria Cunea Larvae Using Convolutional Neural Network

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.