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VAPOR HEAT TREATMENT AND ITS EFFECT ON MELON (Cucumis melo L.) QUALITIES DURING STORAGE Michael Alexander Hutabarat; Rokhani Hasbullah; Mohamad Solahudin
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 8, No 2 (2019): Juni
Publisher : The University of Lampung

Melon is very popular among Indonesians because of its sweet taste and rich in nutrients and also very potential as export comodities. Every export comodities including melon need proper handlings of quarantine to disinfestation pests / diseases. One of quarantine technique which is appliable for melon is vapor heat treatment (VHT). Aims of this research are to make a simulation of heat distribution during VHT process inside melons, to observe VHT technique effects on melon qualities during storage, and to determine the optimum time of VHT process for melon. Finite difference method is used in designing the simulation using Visual Basic 6.0. To observe melon qualities, this research used complete randomized design (CRD) with 4 level of treatment based on VHT process duration which was 10 minutes, 20 minutes, 30 minutes and control every 4 days for 24 days. The result showed that finite difference method can be used for simulating heat distribution inside melon during VHT with coefficient determination (R2) value of 0.9903. Beside that, the result also showed that there were no signifficant difference between each treatments. Based on these results, VHT with 46.5oC temperature and 10 minutes duration time considered as the best treatment.

Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots Yuvicko Gerhaen Purwansya; Mohamad Solahudin; 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

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.

Secondary Metabolites and Antioxidant Activity of Purwoceng (Pimpinella Pruatjan) Root Extracts from Various Hydroponic Planting Techniques Irmanida Batubara; Shadila Fira Asoka; Eni Sumarni; Herry Suhardiyanto; Mohamad Solahudin; Slamet Widodo; Supriyanto Supriyanto; Eti Rohaeti; Yudiwanti Wahyu; Folkes Laumal; Erniati Erniati
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol 12, No 3 (2023): September 2023
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v12i3.641-652

Purwoceng (Pimpinella pruatjan) is Indonesia’s native herbs that grow in the highlands but its existence is hard to find. The reason is that purwoceng is difficult to cultivate. A controlled factor in cultivation, such as hydroponic types and nutrient concentration, can be used as a solution to this problem. Hydroponic types and nutrient concentration treatments can affect the secondary metabolites and antioxidant activity of the purwoceng root extract produced. This study aimed to determine total phenolic and flavonoid content, as well as antioxidant activity in three different hydroponic systems (nonrecirculating drip, recirculating drip, and nutrient film technique (NFT)) and two nutrient concentrations (1.5‰ and 2.0‰). The combination of recirculating drip with low nutrient concentration was the best treatment to produce an extract with high phenolic and flavonoid content. Purwoceng root extracts from nonrecirculating with high nutrient concentrations produced high antioxidant activity. The characteristics of extracts from recirculating with low nutrient concentrations were similar to those from the nonrecirculating drip. In contrast, extracts from recirculating with high nutrient concentrations were closer to extracts from NFT, proven by principal component and heat map analysis. Antioxidant activity related to total phenolic content, also the presence of betaine and bergapten in purwoceng root extracts. Keywords: Flavonoid content, NFT, Nonrecirculating drip, Phenolic content, Recirculating drip

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

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.

Deep Learning-Based Detection for Early Germination Stages of Chili Pepper (Capsicum annuum L) Seedling in Greenhouse Tasmara, Jasmine; Supriyanto, Supriyanto; Solahudin, Mohamad
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.v14i4.1128-1139

Nursery plays an important role on starting chili cultivation, determining the crop health, fertility from disease, and growth performance. Early-stage germination detection is necessary to minimize nursery failure and improve plant health, but manual detection is challenging for large scale nursery in the greenhouse. The aim of this research was to develop an automatic detection model integrated with a You Only Look Once (YOLO) based deep learning algorithm using RGB camera to monitor the chili germination stages. Method to detect germination was YOLO with several steps, included: (1) early stages chili germination images acquisition, (2) datasets preparations, (3) dataset annotation and labeling, (4) model development using deep learning YOLO algorithms, and (5) model testing and validation. The training of 11,423 images was conducted utilizing the YOLOv5 and YOLOv8 algorithms, which categorized into, three classes (germinated, not germinated, and cotyledon appearance). The model was evaluated using mean Average Precision (mAP), precision, accuracy, and recall with the respective values of 0.697, 73%, 75%, and 73% for YOLOv8, and 0.664, 70%, 73%, and 70% for YOLOv5. Both model achieved high accuracy, but YOLOv8 was better to detect and classify chili seedling growth stages than YOLOv5. This study also demonstrated that model can be implemented in real applications integrated with automatic monitoring system included in the model. Keywords: Chili seedling, Deep learning, Detection system, Germination.

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

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

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

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.

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