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All Journal Bulletin of Electrical Engineering and Informatics
Ahmad, Sharaf
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Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector Che Ani, Adi Izhar; Hamdani Mohammad Farid, Mohammad Afiq; Firdhaus Kamaruzaman, Ahmad Shukri; Ahmad, Sharaf; Hadi, Mokh Sholihul
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.5705

Abstract

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.
Co-Authors Che Ani, Adi Izhar Firdhaus Kamaruzaman, Ahmad Shukri Hamdani Mohammad Farid, Mohammad Afiq Mokh Sholihul Hadi