RANCANG BANGUN ALAT MONITORING TANAMAN HIDROPONIK PAKCOY MEMANFAATKAN MIKROKONTROLER DAN TEKNIK COMPUTER VISION

I. G. M. Andi Dipayana, Duman Care Khrisne, W. Setiawan
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Abstract

Technology in agriculture is growing, one of them is hydroponic techniques because it uses anInternet of Things (IoT) based system. One of the plants used in hydroponic techniques is thepakcoy vegetable plant. Pakcoy is one of the vegetables that has the widest distribution in Asia.In addition, the characteristics of pakcoy vegetables are almost the same as mustard greens.Pakcoy vegetables are also easy to cultivate because they only require a small amount of landand a short harvest period. This system is built using a microcontroller and computer visiontechniques as a control center and uses a Raspberry Pi 4 type B mini PC as a visual notificationand is equipped with a dc motor gearbox, L298N motor driver and webcam camera. Thissystem works automatically to control which plants are healthy and which plants are sick. Thisstudy aims to detect diseased plants and healthy plants on pakcoy plants with a hydroponicpakcoy plant monitoring tool that uses a microcontroller and computer vision techniques. Thistool works by taking pictures directly through a webcam camera, then processing them with atrained model. The output of this tool displays the probability value of pakcoy plants showinghealthy plants and sick plants. From the results of testing the validation data using 60 validationdata, based on the accuracy of the detection tool healthy and sick plants were able to correctlyidentify 38 validation data and 22 data were not recognized properly, so the accuracy of thevalue was 63%. and produces an f1-score of 0,87
农业技术正在发展,其中之一是水培技术,因为它使用了基于物联网(IoT)的系统。水培技术中使用的植物之一是白菜蔬菜。椰菜是亚洲分布最广的蔬菜之一。此外,白菜蔬菜的特性与芥菜几乎相同。白菜蔬菜也很容易种植,因为它们只需要很少的土地,收获时间短。本系统以单片机和计算机视觉技术为控制中心,以树莓派4型B型微型PC作为视觉通知,配备直流电机变速箱、L298N电机驱动器和网络摄像头。这个系统自动控制哪些植物是健康的,哪些植物是生病的。本研究的目的是利用微控制器和计算机视觉技术,开发一种水培植物监测工具,检测白豆植株上的病株和健康株。这个工具的工作原理是直接通过网络摄像头拍照,然后用训练过的模型处理照片。该工具的输出显示了pakcoy植物显示健康植物和患病植物的概率值。从使用60个验证数据测试验证数据的结果来看,基于检测工具的准确性,健康和患病植物能够正确识别38个验证数据,22个数据未被正确识别,因此该值的准确性为63%。得到的f1分数是0.87分
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