首页> 外文期刊>Journal of computer sciences >Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning
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Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

机译:基于机器学习的自动秋天粘虫(Spodoptera frugiperda,J.E. Smith)信息素陷阱

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Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of infestation. In addition, the insect pest's abundance and environmental conditions should be predicted as early as possible for integrated pest management to be effective. Consequently, a fall armyworm pheromone trap was deployed as a monitoring tool in the present study. The trap inspection is currently carried out manually every week. The purpose of this paper is to bring automation to the trap. We modify the trap and integrate Internet of Things technologies which include a Raspberry Pi 3 Model B+ micro-computer, Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors powered with an off-grid solar photovoltaic system to capture real-time fall armyworm moth images, environmental conditions and provide real-time indications of the pest occurrences. The environmental conditions include Geographical Positioning System coordinates, temperature, humidity, wind speed and direction. The captured images together with environmental conditions are uploaded to the cloud server where the image is classified instantly using Google's pre-trained InceptionV3 Machine Learning model. Intended users view captured data including prediction accuracy via a web application. Once this smart technology is adopted, the labour-intensive task of monitoring will reduce while stakeholders shall be provided with a near real-time insight into the FAW situation in the field therefore enabling pro-activeness in their management of such a devastating pest.
机译:玉米是满足撒哈拉以南非洲地区人类和牲畜营养需求的主要粮食作物。玉米近来受到秋夜蛾(Spodoptera frugiperda,J.E Smith)的威胁,该夜蛾在该地区造成了相当大的玉米单产损失。防治这种有害生物需要了解侵害的时间,地点和程度。此外,应尽早预测害虫的丰度和环境条件,以使害虫综合治理有效。因此,本研究采用了秋天粘虫信息素诱集器作为监测工具。目前,陷阱检查是每周手动进行的。本文的目的是使陷阱自动化。我们修改了陷阱并整合了物联网技术,其中包括Raspberry Pi 3 B +型微型计算机,Atmel 8位AVR微控制器,3G蜂窝调制解调器以及由离网太阳能光伏系统供电的各种传感器,以捕获实时跌落粘虫蛾的图像,环境条件并提供有害生物发生的实时指示。环境条件包括地理定位系统的坐标,温度,湿度,风速和方向。捕获的图像和环境条件一起上传到云服务器,在该服务器上,使用Google的预先训练的InceptionV3机器学习模型对图像进行即时分类。目标用户可以通过Web应用程序查看捕获的数据,包括预测准确性。一旦采用了这种智能技术,将减少劳动密集型的监测任务,同时应向利益相关者提供有关实地一汽状况的近实时洞察力,从而使他们能够积极主动地管理这种毁灭性害虫。

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