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All Journal Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Supriyanto Supriyanto
IPB University
Agriculture, Biological Sciences & Forestry Engineering

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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

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.
Artificial Neural Network Model for Shallot Disease Severity Prediction Using Drone Multispectral Imagery Angga Firmansyah; Mohamad Solahudin; 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.
Deep Learning-Based Detection for Early Germination Stages of Chili Pepper (Capsicum annuum L) Seedling in Greenhouse Jasmine Tasmara; Supriyanto Supriyanto; Mohamad Solahudin
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

Abstract

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.
Co-Authors Angga Firmansyah Jasmine Tasmara Mohamad Solahudin Yuvicko Gerhaen Purwansya