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All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Angga Firmansyah
IPB University
Agriculture, Biological Sciences & Forestry Engineering

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Design and Performance Test of Brown Rice Germinator with Automatic Environmental Control System for Production of Germinated Brown Rice Ressy Angli Permatasari; Sutrisno Sutrisno; I Wayan Budiastra; Haris Mawardi; Angga Firmansyah; Arfandi Hermawan; Elisa Eka Ari Purwanti Ningsih
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 14, No 1 (2025): February 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i1.171-181

Abstract

A germinator equipped with automatic environmental control system has been developed to produce the high quality of germinated brown rice. The germinator consists of germination chamber, temperature and relative humidity sensors, relays, actuator, and display panel so the germination process can be set up and controlled. The performance test were carried out covering the technical reliability of the system and the capability of germinator to produce germinated brown rice. The test results show that the brown rice germinator with an automatic environmental control system worked very well. The use of water misters and PTC air heaters is able to maintain humidity and air temperature inside germinator. The brown rice germinator can produce germinated brown rice with germination rate more than 80%. The result shows that the brown rice germinator can be used to produce germinated brown rice both for private and commercial use. Keywords: Brown rice, Germination, Humidity, Temperature.
Design and Performance Test of Brown Rice Germinator with Automatic Environmental Control System for Production of Germinated Brown Rice Ressy Angli Permatasari; Sutrisno Sutrisno; I Wayan Budiastra; Haris Mawardi; Angga Firmansyah; Arfandi Hermawan; Elisa Eka Ari Purwanti Ningsih
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 1 (2025): February 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i1.171-181

Abstract

A germinator equipped with automatic environmental control system has been developed to produce the high quality of germinated brown rice. The germinator consists of germination chamber, temperature and relative humidity sensors, relays, actuator, and display panel so the germination process can be set up and controlled. The performance test were carried out covering the technical reliability of the system and the capability of germinator to produce germinated brown rice. The test results show that the brown rice germinator with an automatic environmental control system worked very well. The use of water misters and PTC air heaters is able to maintain humidity and air temperature inside germinator. The brown rice germinator can produce germinated brown rice with germination rate more than 80%. The result shows that the brown rice germinator can be used to produce germinated brown rice both for private and commercial use. Keywords: Brown rice, Germination, Humidity, Temperature.
Artificial Neural Network Model for Shallot Disease Severity Prediction Using Drone Multispectral Imagery Angga Firmansyah; Mohamad Solahudin; Supriyanto Supriyanto
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 2 (2025): April 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i2.623-637

Abstract

Shallot plant diseases can reduce yields by up to 50% of total land area. Currently, shallot plant disease identification relies on direct observation, which is less effective and efficient due to varying intensities of disease and large cultivation areas. This study aims to develop a predictive model for shallot disease severity using multispectral drone imagery, apply Artificial Neural Network (ANN) algorithm to analyze multispectral band data, and evaluate the model's performance. The study used ANN algorithm with multi-layer perceptron regressor, involving following stages such as dataset acquisition, dataset stitching, dataset filtering and feature extraction, model development, and model evaluation. Multispectral data were taken using DJI Mavic 3 Multispectral drone, resulting 696 images per bands that were stitched into orthophoto map. The filtering process of plant objects yielded better model training results compared to unfiltered data. The optimal ANN model structure was identified as 4-6-2-1, with R² value of 0.9194 and MAE value of 0.0618. Model testing results demonstrated that using four input bands (G, R, RE, NIR) provided the best performance with R² value of 0.9194, followed by combination of two bands (R, RE) with R² value of 0.8883. This indicated that the R and RE bands were most strongly correlated with shallot disease severity. Keywords: Drone, Multi-layer perceptron, Multispectral imagery, Plant disease, Shallot.
Co-Authors Arfandi Hermawan Elisa Eka Ari Purwanti Ningsih Haris Mawardi I Wayan Budiastra Mohamad Solahudin Ressy Angli Permatasari Supriyanto Supriyanto Sutrisno Sutrisno