Application of Deep Learning for Fig Fruit Detection in The Wild

Kamaruzaman, Asf; Farid, M.; Izhar, Che Ani Adi; Maruzuki, Mif; S, Ahmad; Habibi, Muhammad Afnan

doi:10.1109/esmarta56775.2022.9935356

Cited by 2 publications

(1 citation statement)

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“…This conventional technique of detection would need a lot of labour and time. Due to the rapid development of algorithms [1] for artificial intelligence and image sensor technology, online automatic fruit recognition has lately emerged as a new trend [2]. They allow farmers to manage and maximize their resources while making wise decisions during harvest [3].…”

Section: Introductionmentioning

confidence: 99%

A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector

Che Ani,

Hamdani Mohammad Farid,

Firdhaus Kamaruzaman

et al. 2024

Bulletin EEI

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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.

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Section: Introductionmentioning

confidence: 99%