Monitoring pest populations in paddy fields is important to effectively implement integrated pest management. Light traps are widely used to monitor field pests all over the world. Most conventional light traps still involve manual identification of target pests from lots of trapped insects, which is time-consuming, labor-intensive and error-prone, especially in pest peak periods. In this paper, we developed an automatic monitoring system for rice light-trap pests based on machine vision. This system is composed of an intelligent light trap, a computer or mobile phone client platform and a cloud server. The light trap firstly traps, kills and disperses insects, then collects images of trapped insects and sends each image to the cloud server. Five target pests in images are automatically identified and counted by pest identification models loaded in the server. To avoid light-trap insects piling up, a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects. There was a close correlation (r=0.92) between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap. Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.

监测稻田中的有害生物种群对于有效实施有害生物综合治理至关重要。捕光器被广泛用于监测全世界的田间害虫。大多数传统的光阱仍然需要人工识别许多被捕获的昆虫中的目标害虫，这非常耗时，费力且容易出错，尤其是在害虫高峰期。在本文中，我们开发了基于机器视觉的水稻光诱害虫自动监测系统。该系统由智能照明灯，计算机或手机客户端平台和云服务器组成。光阱首先捕获，杀死并驱散昆虫，然后收集捕获的昆虫的图像并将每个图像发送到云服务器。通过服务器中加载的有害生物识别模型自动识别并计数图像中的五个目标有害生物。为避免捕光虫堆积，采用振动板和旋转运动的传送带分散被捕虫。有密切的相关性（r = 0.92）在我们的自动和手动识别方法之间，基于来自一个光阱的一年图像的每日害虫数量。现场实验证明了我们的自动光阱监控系统的有效性和准确性。