Deep Learning Model to Predict the Ripeness of Oil Palm Fruit

Isis Bonet, Mario Gongora, Fernando Acevedo, Ivan Ochoa

2024

Abstract

This study explores the application of deep learning, specifically the YOLOv8 model, for predicting the ripeness of oil palm fruit bunch through digital images. Recognizing the economic importance of oil palm cultivation, precise maturity assessment is crucial for optimizing harvesting decisions and overall productivity. Traditional methods relying on visual inspections and manual sampling are labor-intensive and subjective. Leveraging deep learning techniques, the study aims to automate maturity classification, addressing limitations of prior methodologies. The YOLOv8 model exhibits promising metrics, achieving high precision and recall values. Practical applications include deployment in production areas and real-time field scenarios, enhancing overall production processes. Despite excellent metric results, the model shows potential for further improvement with additional training data. The research highlights the effectiveness of YOLOv8 in automating the ripeness classification oil palm fruit bunches, contributing to sustainable cultivation practices in diverse agricultural settings.

Paper Citation

in Harvard Style

Bonet I., Gongora M., Acevedo F. and Ochoa I. (2024). Deep Learning Model to Predict the Ripeness of Oil Palm Fruit. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1068-1075. DOI: 10.5220/0012434600003636

in Bibtex Style

@conference{icaart24,
author={Isis Bonet and Mario Gongora and Fernando Acevedo and Ivan Ochoa},
title={Deep Learning Model to Predict the Ripeness of Oil Palm Fruit},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1068-1075},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012434600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Deep Learning Model to Predict the Ripeness of Oil Palm Fruit
SN - 978-989-758-680-4
AU - Bonet I.
AU - Gongora M.
AU - Acevedo F.
AU - Ochoa I.
PY - 2024
SP - 1068
EP - 1075
DO - 10.5220/0012434600003636
PB - SciTePress