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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Keteknikan Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Warta IHP (Warta Industri Hasil Pertanian) Jurnal Irigasi Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Indonesian Journal of Electrical Engineering and Computer Science
Slamet Widodo
Department Of Mechanical And Biosystem Engineering, Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Prediction of Phenotypic Parameters of Sugarcane Plants Based on Multispectral Drone Imagery and Machine learning Febri Hasskavendo; Mohamad Solahudin; Supriyanto Supriyanto; Slamet Widodo
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.
Portable Near-Infrared Spectroscopy and Support Vector Regression for Fast Quality Evaluation of Vanilla (Vanilla planifolia) Widyaningrum Widyaningrum; Yohanes Aris Purwanto; Slamet Widodo; Supijatno Supijatno; Evi Savitri Iriani
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.v14i2.515-526

Abstract

Vanilla (Vanilla planifolia) is a high-value agricultural product, with its quality influenced by essential factors such as moisture and vanillin content. Conventional techniques for evaluating these characteristics are inefficient, require sample destruction, and are impractical for swift assessments. This research explores the feasibility of using portable Near-Infrared (NIR) spectroscopy combined with Support Vector Regression (SVR) to enable quick and noninvasive property prediction. Spectral information was obtained from vanilla samples using two portable NIR instruments, SCiO (740–1070 nm) and Neospectra (1350 2550 nm). Preprocessing techniques such as normalization, SNV, MSC, first derivative, first derivative-SNV, and first derivative-MSC were applied. For moisture content prediction, SCiO achieved an R² of 0.768, an RMSE of 4.720%, an RPD of 2.075 and an RER 10.197 using Min-Max normalization, while Neospectra yielded an R² of 0.758, an RMSE of 5.161%, an RPD of 2.033 and an RER 9.325 with MSC preprocessing. In contrast, predicting vanillin concentration proved more challenging, with SCiO achieving moderate accuracy with an R² 0.406, an RMSE 0.379%, an RPD 1.297, an RER 5.039, and Neospectra demonstrating limited performance with an R² 0.172, an RMSE 0.576%, an RPD 1.098 and an RER 3.315. These findings highlight the potential of portable NIR spectroscopy as a practical tool for assessing vanilla quality, particularly for moisture content, in industrial and field applications. Keywords: Moisture content, Portable NIR spectroscopy, Support vector regression, Vanilla planifolia, Vanillin content.
Fluorescence Imaging as a Non-Destructive Method for Aflatoxin Detection in Corn Kernels: Recent Advances and Challenges Sri Handayani Nofiyanti; Usman Ahmad; Efi Toding Tondok; Slamet Widodo
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.v14i2.714-731

Abstract

Fluorescence imaging has developed as a promising non-invasive method for identifying aflatoxin contamination in agricultural commodities, especially corn kernels. This paper examines current improvements in fluorescence imaging technologies, highlighting its potential to improve food safety through swift and precise detection of mycotoxins. The paper examines the basics of fluorescence, the necessary setup for optimal imaging, and the issues related to background fluorescence interference, sensitivity, and the construction of calibration models. Although there are some limitations, fluorescence imaging presents considerable advantages, such as cost-efficiency and the capacity to obtain concurrent spectral and spatial data. Proposed future research objectives include the validation of imaging systems using naturally contaminated samples, the optimization of imaging parameters, and the integration of machine learning techniques to enhance data processing. By overcoming existing constraints and utilizing technical progress, fluorescence imaging can serve as an essential instrument in the detection of aflatoxin contamination, hence enhancing food safety. Keywords: Aflatoxin, Detection, Fluorescence imaging, Food safety, Machine learning.
Co-Authors - Nurfaijah - Rudiyanto Agita Rakhmawati Ahmad Safrizal Akeme Cyril Njume Andi Muhammad Akram Mukhlis Anggie Yulia Sari Aris Purwanto Ayu Putri Ana Budi Indra Setiawan Bung Daka Putera Chusnul Arif DEWI APRI ASTUTI Didik M Rifqi Edy Hartulistiyoso Efi Toding Tondok Evi Savitri Iriani Evi Savitri Iriani Fadilah Khairani Febri Hasskavendo Folkes Eduard Laumal Hafiz Fajrin Aditama Herry Suhardiyanto I Wayan Budiastra Masaru Mizoguchi Masyitah Masyitah Masyitah Mohamad Solahudin Mu'minah Mustaqimah Muhammad Kalili Muhammad Naufal Rauf Ibrahim Nofiyanti, Sri Handayani Nur Aini Iswati Hasanah Nur Aini Iswati Hasanah Rizky Wiradinata Supijatno Supriyanto Supriyanto Sutan Muhammad Sadam Awal Tjahya Muhandri Tutik Lestari Usman Ahmad Widyaningrum Widyaningrum Widyaningrum Widyaningrum