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All Journal HAYATI Journal of Biosciences Jurnal Keteknikan Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Indonesian Journal of Electrical Engineering and Computer Science ALACRITY : Journal Of Education
Mohamad Solahudin
Department Of Mechanical And Biosystem Engineering, Faculty Of Agricultural Engineering And Technology, IPB University, Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mechanical Engineering Other

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Non-destructive Prediction of Brix Value in Sugarcane Based of Portable NIR Spectroscopy Ahmar, Afdhalul; Solahudin, Mohamad; Widodo, Slamet
Jurnal Keteknikan Pertanian Vol. 12 No. 3 (2024): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.012.3.424-437

Abstract

The quality of sugarcane in the plantation is the most important information for farmers and sugar factories for assessing the maturity of sugarcane and determining the optimal harvest schedule. The brix value is used as a quality index in the sugar industry and is an important parameter for the evaluation of cane quality and maturity. Traditional methods of determining brix involve time-consuming and labor-intensive processes, often involving destructive sampling. To overcome these challenges, this study proposes a non-destructive approach using portable near-infrared (NIR) spectroscopy to predict the sugar content in sugarcane stalks. The main objective of this study was to develop a nondestructive prediction model for the brix value in sugarcane using portable NIR spectroscopy. Data processing involved two models: Partial Least Squares (PLS) and an Artificial Neural Network (ANN), along with various data pre-treatment techniques. The PLS model showed an improvement in prediction accuracy with data pre-treatment, especially with the Savitzky-Golay method (R2 = 0.755, RMSEP = 1.22%, RMSEP = 1.43%, CV = 6.13%, and RPD = 2.02). In addition, the ANN model combined with Principal Component Analysis (PCA) showed high predictive performance when sugarcane was 11 months old (R2 = 0.797, RMSEC = 0.56%, RMSEP = 0.87%, CV = 3.04%, and RPD = 2.96).
Adaptive-Historical Energy-Efficient Temperature Control for Tropical Greenhouses Laumal, Folkes; Suhardiyanto, Herry; Solahudin, Mohamad; Widodo, Slamet
Jurnal Keteknikan Pertanian Vol. 13 No. 1 (2025): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.013.1.55-73

Abstract

Maintaining an optimal microclimate is essential for efficient operation of tropical greenhouses, particularly under fluctuating weather conditions. This study proposes an adaptive energy-efficient model for regulating air temperature in tropical greenhouses using historical climate data. The model optimizes the fan rotation speeds via an inverter to meet the temperature targets while minimizing energy consumption. Key methodologies include climate data analysis, development of a predictive model for indoor air temperature using Artificial Neural Networks, and optimization of fan speed control. The model achieved high predictive accuracy, with an RMSE of 0,02 and an R² of 0,96. The practical implementation demonstrated effective temperature control, with fan speeds ranging between 30 and 40 Hz during cloudy periods and 50 Hz in sunny conditions. Notably, the system reduced electricity consumption by 33,93% during cloudy weather and 18,54% in sunny weather, showing its potential for significant energy savings. This data-driven adaptive model approach is highly suited for tropical greenhouses experiencing dynamic climatic variations and offers a sustainable and efficient solution for greenhouse microclimate management.
Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots Purwansya, Yuvicko Gerhaen; Solahudin, Mohamad; Supriyanto, Supriyanto
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 2 (2024): June 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i2.350-360

Abstract

As the demand for shallot increases, wide-scale cultivation area must be managed efficiently. However, shallot productivity decreases every year because of plant diseases. Fusarium disease has an intensity up to 60% and can affect yield losses up to 50%. This study was conducted to develop the fusarium disease detection system for shallot using deep learning model and analyze the effect of preprocessing and augmentation adjustment. This study used YOLOv5 deep learning algorithm consisting of the following stages: (1) dataset acquisition, (2) dataset annotation, (3) dataset preprocessing and augmentation, (4) dataset training and validation, and (5) model testing and evaluation. A total 9,664 annotated dataset was trained to YOLOv5m pre-trained weights. Based on testing and evaluation results, precision, recall, and mean average precision (mAP) metrics of the model without preprocessing and augmentation were 55.5%; 54%; and 48.3% respectively. Metric values of the model were increased to 57.6%; 58.4%; and 54.1% respectively with adjustment of preprocessing and augmentation combination process. Percentage increase in metrics when compared to the control model for each value of precision, recall, and mAP were 2.1%; 4.4%; and 5.8%. This shows a significant impact on the addition of preprocessing and augmentation processes that match the characteristics of the dataset to increase the value of model performance. Keywords: Augmentation, Deep learning, Fusarium, Shallot.
Prediction of Phenotypic Parameters of Sugarcane Plants Based on Multispectral Drone Imagery and Machine learning Hasskavendo, Febri; Solahudin, Mohamad; Supriyanto, Supriyanto; Widodo, Slamet
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.v13i4.1182-1195

Abstract

Measuring phenotypic parameters is important in evaluating the productivity of sugarcane. Existing manual measurements are considered less efficient, so a better alternative method is needed. This research aims to explore the potential of using multispectral drone imagery and machine learning to estimate phenotypic parameters of sugarcane plants that are efficient, accurate, inexpensive, and support sustainable agricultural practices. Spectrum data captured by drones, namely Green, Red, RedEdge and NIR are used as inputs to estimate phenotypic parameters including brix value, number of stands, stem diameter, and plant height. Based on the results of machine learning model development, the ANN algorithm model is most effective in predicting Brix Value with R2 0.74 and RMSE 0.06 and number of stands with R2 0.68 and RMSE 2.13. All models could not predict stem diameter and plant height well. The best model to predict plant height was obtained by RF algorithm with R2 0.53 and RMSE 14.09. SVR algorithm was the best model to predict plant diameter with R2 0.39. and RMSE 0.49. This indicates that the effectiveness of an algorithm depends on the specific parameter being predicted and there is no dominant algorithm for all phenotypic parameters. Keywords: Machine learning, Multispectral drone imagery, Phenotypic parameter, Plant productivity, Sugarcane.
Artificial Neural Network Model for Shallot Disease Severity Prediction Using Drone Multispectral Imagery Firmansyah, Angga; Solahudin, Mohamad; 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 Agus Buono Ahmar, Afdhalul Alvin Fatikhunnada Alvin Fatikhunnada Eni Sumarni Eni Sumarni Erniati Erniati Erniati Eti Rohaeti Eti Rohaeti Fadhilah Khairani, Fadhilah Fahrezi, Rafli Arya Febri Hasskavendo Fenry Winna Mutawally Firmansyah, Angga Folkes Eduard Laumal Folkes Laumal Folkes Laumal folkes laumal, folkes Fuad Heru Setiawan Giska Priaji Gumilang Agus Gozali Hasskavendo, Febri Herry Suhardiyanto Heru Sukoco I Wayan Astika I Wayan Budiastra Ihsan, Mahlil Nurul Irmanida Batubara Jayawarsa, A.A. Ketut Kahfi Gunardi Kania Dewi, Kania Karlisa Priandana Khairani, Fadilah Khoirul Umam Kiswanto S. Heri Kudang Boro Seminar Lilis Sucahyo Liyantono . Medria Kusuma Dewi Hardhienata Michael Alexander Hutabarat Mohamad Iqbal Suriansyah Mohamad Yanuar Jawardi Purwanto Muhammad Naufal Rauf Ibrahim NANIK PURWANTI Nelwan, Leopold Oscar Nurbaiti Araswati Omil Charmyn Chatib Prasetyo, Tegar Adi Prastono, Haryo Purwansya, Yuvicko Gerhaen Putri, Sindi Lestari Rena Nurista Riyanti Riyanti Rokhani Hasbullah Romadhon, Akbar Septiadi, Ananda Putra Shadila Fira Asoka Shadila Fira Asoka Shandra Amarillis Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Slamet Widodo Sucahyo, Lilis Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Supriyanto Tasmara, Jasmine Tineke Mandang WULANDARI Y. Aris Purwanto Yanti, Delvi Yudi Chadirin Yudiwanti Yudiwanti Wahyu E. Kusumo Yuvicko Gerhaen Purwansya