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Improved YOLOv8 Segmentation Model for the Detection of Moko and Black Sigatoka Diseases in Banana Crops with UAV Imagery

Identification

Clé publique

radar:r-8f1ead18b5780ff6526e1b65ffdf2b60

Auteurs

Byron Oviedo · Cristian G. Zambrano-Vega · Ronald Oswaldo Villamar-Torres · Danilo Javier Yánez Cajo · Kevin Cedeño Campoverde

Année déclarée

2025

Langue déclarée

en

Type source

article

Première détection

2026-11-13T12:45:03.773010+00:00

Dernière observation des métadonnées

2026-11-13T12:45:03.773010+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Universidad Técnica Estatal de Quevedo · Clínica Medellín SAS

Pays d’affiliation déclarés

CO · EC

Résumé documentaire — langue originale

Banana (Musa spp.) crops face severe yield and economic losses due to foliar diseases such as Moko disease and Black Sigatoka. In Ecuador, Moko outbreaks have increasingly devastated banana plantations, threatening one of the country’s most important export commodities and putting significant pressure on local producers and the national economy. Traditional field inspection methods are labor-intensive, subjective, and often ineffective for timely disease detection and containment. In this study, we propose an improved deep learning-based segmentation approach using YOLOv8 architectures to automatically detect and segment Moko and Black Sigatoka infections from unmanned aerial vehicle (UAV) imagery. Multiple YOLOv8 configurations were systematically analyzed and compared, including variations in backbone depth, model size, and hyperparameter tuning, to identify the most robust setup for field conditions. The final optimized configuration achieved a mean precision of 79.6%, recall of 80.3%, mAP@0.5 of 84.9%, and mAP@0.5:0.95 of 62.9%. The experimental results demonstrate that the improved YOLOv8 segmentation model significantly outperforms previous classification-based methods, offering precise instance-level localization of disease symptoms. This study provides a solid foundation for developing UAV-based automated monitoring pipelines, contributing to more efficient, objective, and scalable disease management strategies.

Identifiants

OPENALEX

https://openalex.org/W4413775410

DOI

10.3390/technologies13090382

Repères documentaires

Famille documentaire

article

Types déclarés par les sources

article

Éditeurs

Multidisciplinary Digital Publishing Institute

Les mentions d’entités ne constituent pas une preuve de reconnaissance juridique. La juridiction étudiée est distincte du pays d’affiliation des auteurs.

Sources

OpenAlex · Document 1

• Document · https://doi.org/10.3390/technologies13090382

Statut documentaire

Pertinence à examiner

Bibliothèque

Cette identité n’est pas encore intégrée à la Bibliothèque juridique.