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Journal : Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)

Design of Scheduled Fog Irrigation System with ESP 32 in Mustard Seedbed (Brassica juncea L.) Sumarsono, Joko; Widhiantari, Ida Ayu; De Side, Gagassage Nanaluih; Widhiantara, I Ketut Manik
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 4 (2024): December 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i4.1109-1120

Abstract

Plants receive water from the fog irrigation system in the form of tiny, mist-like water particles. Currently, irrigation technology has advanced, nurseries can be automatically irrigated. In this research, the ESP32 microcontroller is used to create the electronic circuits for the automation of the fog irrigation control system in a mustard green nursery. This study's methodology combines direct field observation with an experimental or trial approach. Water flow (ml/minute), water use efficiency (%), plant height (cm), number of leaves, and scheduled fog irrigation system performance are among the measured parameters. In this investigation, four different treatments were used: manual irrigation using a watering can, two-time, three-time, and one-time irrigation. All is going according to plan with this mist watering system. With an average height of 4.58 cm and an average number of leaves of 3.24, the mist irrigation system had the highest water use efficiency of 77.35% during the test. It was also found that mist irrigation produced the highest amount of mustard leaves in the three-times-per-day treatment. Keywords: ESP32, Fog irrigation, Green mustard, Nurseries.
Design and Performance Test of Corn Seeder Integrated with Fertilizer Applicator Ansar, Muhammad Asshidiq; Ansar, Ansar; De Side, Gagassage Nanaluih
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 3 (2025): June 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i3.911-919

Abstract

Along with the increasing demand for corn in the country, the use of agricultural tools and machines to increase corn productivity is very important. Therefore, this study aims to design and test the performance of a corn seeder integrated with fertilizer applicator. This tool is designed using the principles of agricultural mechanics. The research stages began with conducting a feasibility study, designing a prototype of the tool, then conducting performance testing on agricultural land. The results showed that the majority of farmers considered this tool very effective and efficient to use compared to a digging tool. The results of the performance test on farmers' land also showed that this tool was able to plant corn seeds with uniform depth and spacing and distribute fertilizer evenly. All components of the corn seeder function properly, so it is suitable for use to increase the productivity of corn farmers' land. This corn seeder has met the category requirements as a corn seeder and fertilizer applicator with a manual operating system. The application of this corn seeder has the potential to increase land productivity and can facilitate the process of planting corn seeds. Keywords: Corn seeders, Corn, Land productivity, Planting holes, Respondents.
Comparative Analysis of Growth Models for Lettuce (Lactuca sativa) in a Plant Factory under Red-Blue LED Treatment Putra, Guyup Mahardhian Dwi; De Side, Gagassage Nanaluih; Setiawati, Diah Ajeng; Kurniati, Nia
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 4 (2025): August 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i4.1452-1464

Abstract

The growth of lettuce (Lactuca sativa) in controlled environments such as Plant Factories is highly influenced by lighting, particularly under red-blue (RB) LED treatment. Accurate growth prediction models are essential for optimizing yield. This study compared four models linear, polynomial, logistic, and Gompertz to determine the best predictor of leaf area expansion. Leaf area measurements over 30 days were analyzed using Easy Leaf Area software. Results showed that the Gompertz model consistently outperformed others with the lowest Mean Absolute Percentage Error (MAPE) of 14.55% (slow), 39.51% (medium), and 29.13% (high), and the highest R² values of 0.99 across all growth categories. In contrast, linear and polynomial models exhibited extremely high MAPE values, exceeding 300% in most cases. The study concludes that the Gompertz model is the most accurate and biologically realistic for modeling lettuce growth in Plant Factory systems, offering robust predictive capability for sustainable precision agriculture.