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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Instrumentation system for data acquisition and monitoring of hydroponic farming using ESP32 via Google Firebase Prisma Megantoro; Rizki Putra Prastio; Hafidz Faqih Aldi Kusuma; Abdul Abror; Pandi Vigneshwaran; Dimas Febriyan Priambodo; Diaz Samsun Alif
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp52-61

Abstract

This article discusses the design of a hydroponic planting process monitoring system based on the internet of things. This device uses an ESP32 microcontroller board as the main controller. The parameters that were monitored and acquired were the conditions of the hydroponic growing media. Those parameters are; water pH, water temperature, water turbidity level, and ambient air temperature and humidity. The five parameters are measured by analog sensors integrated with the ESP32. These parameters affect the growth process and the quality of crop yields. This article also describes the calibration method for each sensor used for parameter measurement. Then the monitoring of these parameters is carried out by utilizing a real-time database, namely Google Firebase. This platform is very suitable for all IoT-based monitoring and control applications. Measurement result data is uploaded and saved to the real-time database. Then paired by Android-based applications. This application was created to be used by hydroponic farmers who use this device. Thus the results of monitoring can be used to optimize the process of growing hydroponic plants.
Selection of autofocus algorithms for printed circuit board automated optical inspection system Rizki Putra Prastio; Rodik Wahyu Indrawan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp856-865

Abstract

This paper presents an examination study of 11 autofocus algorithms for printed circuit board (PCB) automated optical inspection (AOI). A selection of an optimal algorithm for that application based on some criteria was carried out. Unlike microscopy, PCB optical inspection does not require very high magnification. The object in this work was also different from that of microscopy and thus influenced the image features. We analyzed 47 PCB images, size of 640×480, sequentially captured every 1 mm in the z-direction. This work utilized USB digital microscope, and the magnification was set at ten times. Each algorithm calculated the sharpness values of the image sequences, and the plot of the sharpness profile was created. Moreover, the research also carried out experiments in several strategies, including image resizing and applying the non-local means (NLM) denoising filter to assess the algorithm performance in different situations. The algorithms were examined and ranked based on five criteria, i.e., computation time, full width at half maximum (FWHM), accuracy, number of half maxima, and range. The experimentation results showed that the Brenner gradient worked best for analyzing images both in their original dimension or resized images.