Effect of Preprocessing and Augmentation Process in Development of a Deep Learning Model for Fusarium Detection in Shallots
DOI:
https://doi.org/10.23960/jtep-l.v13i2.350-360Abstract
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.
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