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All Journal Teknotan: Jurnal Industri Teknologi Pertanian Jurnal AGROTEKNOLOGI Journal of Tropical Soils Berkala Ilmiah Pertanian Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Teknologi Pertanian Andalas Jurnal Penelitian Sains dan Teknologi Indonesia
Bambang Marhaenanto
Jurusan Teknik Pertanian, Fakultas Teknologi Pertanian, Universitas Jember
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Deep Neural Network-Based Estimation of Irrigation Water Requirements for Verticulture and Its Application in Irrigation Management Suhardi; Dafik; Agustin, Ika Hesti; Marhaenanto, Bambang
Jurnal Keteknikan Pertanian Tropis dan Biosistem Vol. 14 No. 2 (2026): August 2026
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/

Abstract

Vertical farming is a crop cultivation system with a tiered planting medium configuration designed to optimize accessibility, maintenance, and harvesting efficiency. The implementation of modern technology based on environmental sensors allows real-time monitoring of microclimate parameters to support precise irrigation management through estimation of evapotranspiration rates (ETo). This study aims to evaluate and estimate ETo values in vertical farming systems using a DNN architecture. Estimation is carried out through Python programming language simulations on the Google Colaboratory platform using a pre-trained DNN model (4 hidden layers) based on input data of average temperature (Tmean) and average relative humidity (RHmean) over a 4-hours duration. The implemented DNN model was validated against actual ETo data in previous studies to ensure the reliability of predictions. The results show that DNN-based evapotranspiration values are significantly influenced by temperature and relative humidity factors. Furthermore, evapotranspiration values, plant growth phases, and planting area are variables needed to calculate irrigation water requirements in the vegetative, generative, and final phases, which require 6.41 liters, 22.85 liters, and 21.73 liters, respectively. Thus, the use of the validated DNN model is proven to be a reliable predictive instrument for precisely determining crop water requirements to achieve more efficient irrigation management.
Co-Authors Agus Dharmawan Cahyoadi Bowo Dafik Deddy Wirawan Soedibyo Diah Meyshita Utari Fathan Edy Purwanto Firdiyanti, Fina I Gusti Bagus Wiksuana Idah Andriyani Ika Hesti Agustin, Ika Hesti Isran Mohamad Pakaya Miftahul Farid Mohammad Hasan Nita Kuswardhani Rudiati Evi Masithoh Siswoyo Soekarno Soni Sisbudi Harsono Soni Sisbudi Harsono, Soni Sisbudi Sri Wahyuningsih Suhardi Suhardi Suhardi Tiarawardani, Dwi Anggi Ade