适应场景光照变化的桔小实蝇诱捕监测系统优化设计与试验.

Bactrocera Dorsalis is a type of invasive pests and causes serious damage to many important economic crops. In order to monitor Bactrocera Dorsalis accurately, and to reduce the misjudgment of light-induced problem, a remote monitoring system which combined a hardware equipment and a software system was designed. The hardware equipment was optimized based on our preliminary design for Bactrocera Dorsalis trapping. By improving the shading system, solar devices and 4G communications devices, the hardware equipment now can be self-powered and integrate the insect pest information collection, processing and transmission as a whole functionality. In addition, this paper presents a good human-computer interaction software system, which includes the Bactrocera Dorsalis monitoring programs, the server and the client-side. The monitoring program can monitor the trapping process, precisely calculate the number of Bactrocera Dorsalis, and automatically transmit the results to the remote server or store it in a local storage card. Users are convenient to obtain the monitoring information through the client-side. In order to improve the accuracy of the Bactrocera Dorsalis detection algorithm(BDDA) and achieve the requirements of real-time systems, an algorithm named Bactrocera Dorsalis detection algorithm under lighting variations(BDDA-LV) was proposed. The BDDA-LV began by selecting appropriate background model according to the light conditions. Then the background difference method was used to extract Bactrocera Dorsalis target preliminarily. Median filtering and morphological filtering for the image was used to reduce white noise, and eliminate holes in the target area to improve the image quality. The image was then divided into blocks based on the adjacent pixels of the image and used these blocks for Geometric feature matching, so that the Bactrocera Dorsalis area can be extracted. Both BDDA-LV algorithm and BDDA algorithm were tested by two sets of data. These data came from the pre-acquired image dorsalis, and was divided into two datasets, one of which was under the moderate influence of light and the other one was under the severe influence of light. Each dataset had 150 images. In the influence of moderate lighting variation, the error rate of BDDA-LV algorithm was 7.21% and 23.07% lower than BBDA algorithm, and its computing time was 41.7% of the BBDA algorithm. In the influence of strong lighting variation, the error rate of BDDA-LV algorithm was 12.4% and 23.07% lower than the BBDA algorithm, and its computing time was 42.2% of the BBDA algorithm. By the long-term tests of the system in the Guangzhou Yangtao Park, the system ran stably and no power outage occurred. A comparative analysis of monitoring system records and artificial counting were carried out from June 18 to 24, 2015. The monitoring system counting was 1634 while artificial counting was 1613. The accuracy of the monitoring system was 98.7%, which improved 3.8% than the preliminary system. The experiments showed that the accuracy and efficiency of the BDDA-LV were higher than the original BBDA algorithm, and the whole system had higher precision and stability. This system can also easily provide accurate pest monitoring information to the regional monitoring personnel in real time, which improves work efficiency. At the same time, the system can provide information support for agricultural researchers to study on the insect activity. This system is thought to be valuable and applicable. [ABSTRACT FROM AUTHOR]