An Automatic Monitoring System for Pest Management in an Orchid Greenhouse Using the Image Processing Technology

• Main Authors: Shih-Hao Yeh, 葉士豪
• Other Authors: Cheng-Ying Chou
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/59470534644780859150

碩士 === 國立臺灣大學 === 生物產業機電工程學研究所 === 104 === Orchids are one of the essential commercial crops in the world. In Taiwan, according to the official statistics, the orchid has a large proportion of the crop planted in export and the orchid export reached 1.83 million USD in 2014. Moreover, orchids are easily damaged by pests, such as dark-winged fungus gnats (Bradysia sp.), resulting in growth retardation or damaged leaves and eventually influencing the price of orchids. To reduce the loss caused by pests and the cost of manual labor, this study developed an automatic monitoring system for pests based on Internet of Things (IoT) to assist floral famers in pest prevention. Two main functions of the proposed system deployed in an orchid greenhouse nursery are environmental monitoring and pest monitoring. In environmental monitoring, a wireless sensor network is responsible for measuring ambient temperature and relative humidity. In pest monitoring, sticky papers with the yellow color of a specially designed wavelength are utilized to attract pests. A camera module is responsible for capturing the images of the papers in a fixed schedule. Furthermore, the images are transmitted to an FTP server. A pest counting algorithm based on light source correction, adaptive binarization, the Canny edge detection and noise reduction is responsible for calculating the number of pests. In addition, the proposed system can automatically change the sticky papers when the number of the pests on the papers exceeds a designed threshold. From Aug. 16 to Nov. 30, 2015, the proposed system has obtained 107 samples, the root mean square error is 2.06, the relative error is 4.91 % ± 0.86 % at a 95 % confidence level. The experimental results show that the counting accuracy of the pest counting algorithm is high. In the future, the sensing data will be utilized to find the relation between the pests and the environment. It is expected that using the proposed monitoring system can provide prescriptive cultivation decisions to farmers based on IoT such that they can prevent pests by using scientific technologies to obtain high yields.