UDTR Global Rights of Nature Radar

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

Identification

Public canonical key

radar:r-8f1ead18b5780ff6526e1b65ffdf2b60

Authors

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

Reported year

2025

Reported language

en

Source type

article

First detected

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

Last metadata observation

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

Last HTTP verification

Not checked

Institutions reported by the source

Universidad Técnica Estatal de Quevedo · Clínica Medellín SAS

Reported affiliation countries

CO · EC

Documentary abstract — original language

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.

Identifiers

OPENALEX

https://openalex.org/W4413775410

DOI

10.3390/technologies13090382

Documentary context

Documentary family

article

Source-reported types

article

Publishers

Multidisciplinary Digital Publishing Institute

Entity mentions are not evidence of legal recognition. Jurisdiction studied is separate from author affiliation country.

Sources

OpenAlex · Record 1

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

Documentary status

Relevance pending

Library

This identity is not yet included in the Legal Library.