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Development of a Raspberry Pi based pest detection device for use in livestock feed storage systems

机译:用于畜禽PI基于覆盆子PI的畜禽饲料储存系统的开发

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Stored feed is frequently damaged by rats. In this study, we made use of the compact "Raspberry Pi" computer to develop an animal damage countermeasure. We also attempted to make the automatic photographing apparatus for dark environments. This deviceincluded a Raspberry Pi, infrared camera module, MOSFET. infrared LED floodlight, and pyroelectric infrared ray sensor. The operation of the apparatus is as follows: When the pyroelectric infrared sensor is triggered by the presence of an animal and thesignal becomes HIGH, a signal of HIGH is output from GPIO of the Raspberry Pi and a voltage of 12 V is applied to the MOSFET to make it function as a relay circuit. The infrared LED projector then lights up. At the same time the device starts taking a still image or video using the infrared camera that is connected to the Raspberry Pi. The apparatus succeeded in shooting a movie of a rat, and it successfully took still pictures. We installed the machine-learning library "TensorFlow " on the Raspberry Pi and performed image recognition of murine images using a deep learning program. When the target animal occupies a small part of the image, the image recognition results tend to be erroneous; however, enlarging the area of interest results in more accurate detection. In addition, erroneous judgment was often made in infrared camera images. The time required for the determination was 15 to 45 seconds for the Pi 3 Model B. These results show that it is possible to create a low-cost pest countermeasure device or robot controlled by a Raspberry Pi, and capable of various responses and input processing.
机译:储存的饲料经常被大鼠损坏。在这项研究中,我们利用紧凑的“覆盆子PI”计算机来发展动物损坏对策。我们还试图为黑暗环境制作自动拍摄设备。这是一种覆盆子PI,红外相机模块,MOSFET。红外LED泛光灯和热电红外线传感器。装置的操作如下:当通过动物的存在触发热电红外传感器并且重生变高时,从覆盆子PI的GPIO输出高的信号,并且将12V的电压施加到MOSFET上使其用作继电器电路。然后红外LED投影仪亮起。同时,设备使用连接到覆盆子PI的红外摄像头开始休息静止图像或视频。该装置成功地拍摄了一部大鼠的电影,它成功地拍摄了静态图片。我们在Raspberry Pi上安装了机器学习库“Tensorflow”,并使用深度学习程序执行了鼠图像的图像识别。当目标动物占据图像的一小部分时,图像识别结果往往是错误的;然而,扩大感兴趣区域导致更准确的检测。此外,错误的判断通常在红外相机图像中进行。对于PI 3模型B,测定所需的时间为15至45秒。这些结果表明,可以创建由覆盆子PI控制的低成本害虫对策装置或机器人,并且能够提供各种响应和输入处理。

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