Authors:
Isis Bonet
1
;
Mario Gongora
2
;
Fernando Acevedo
3
and
Ivan Ochoa
4
Affiliations:
1
Universidad EIA, Envigado, Colombia
;
2
Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, U.K.
;
3
Soluciones Integrales TIC Group S.A.S.
;
4
UNIPALMA de Los Llanos S.A, Meta, Colombia
Keyword(s):
Fruit Ripeness Classification, Oil Palm, YOLO.
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 oi
l palm fruit bunches, contributing to sustainable cultivation practices in diverse agricultural settings.
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