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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/ub.jkptb.2026.014.02.03

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.
Estimating Leaf Area from Digital Images Using Numerical Integration Method Dharmawan, Agus; Alam, Ahmad Mutafakkir; Marhaenanto, Bambang; Soedibyo, Dedy Wirawan
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/ub.jkptb.2026.014.02.13

Abstract

Leaves are essential for photosynthesis and plant productivity, making leaf measurement critical for assessing growth and health. Traditional measurement techniques provide useful and interpretable results but are time?consuming and risk damaging fragile leaves. This study aims to combine digital image analysis with numerical integration method, particularly Newton-Cotes algorithms including the Riemann sums, the composite trapezoid rule, Simpson’s 1/3 and 3/8 rules, Boole’s rule, and the six point NC rule for leaf area estimation. The leaf area is estimated by partitioning and summing the area of curve?bounded objects from different number of subdivisions. A total of 86 leaf samples were collected, and their accuracy and agreement were compared against the grid method. Pearson correlation coefficient was employed to assess the strength of the relationship, while paired samples t-test and Bland–Altman plot was used to evaluate the mean difference and the level of agreement between the two methodologies, respectively. Our findings revealed that higher partition sizes enhanced accuracy, reduced integration error, lowered RMSE, and produced estimates highly consistent with reference leaf area, supported by a Pearson correlation coefficient (r) of 0.999 and non?significant t-test results (?>0.05). The Bland–Altman analysis confirmed narrower variability at higher partitions resulting near?perfect agreement, particularly under the Riemann rule. Finally, leaf area estimation using digital image analysis combined with Newton–Cotes algorithms provide accurate, efficient, and reliable results, demonstrating strong agreement, statistical consistency, and practical scalability for leaf analysis.
Co-Authors Agus Dharmawan Alam, Ahmad Mutafakkir Cahyoadi Bowo Dafik Deddy Wirawan Soedibyo Dedy 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