Proceedings of the International Conference on Communication and Applied Technologies 2025 (ICOMTA 2025)

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Detecting Pests Affecting the AAB Muse Using an Image-recognising Mobile Application

Authors
Ariosto Vicuña Pino1, *, Jessica Ponce Ordoñez1, Bryan Steven Cortez Chichande1, Jaime Mora Vera1
1Quevedo State Technical University, Av. Quito km. 11/2 vía a Santo Domingo, Quevedo, Ecuador
*Corresponding author. Email: avicuna@uteq.edu.ec
Corresponding Author
Ariosto Vicuña Pino
Available Online 22 October 2025.
DOI
10.2991/978-94-6463-868-4_2How to use a DOI?
Keywords
Plantain; disease; computer vision; CNN; mobile application
Abstract

Early detection of pests in Musa AAB (banana) crops is essential to maximise crop production. However, farmers’ lack of experience can limit their ability to identify and control these pests. Constant advances in agricultural technology and the incorporation of software tools have provided efficient solutions to various challenges in the agricultural sector, while optimising the use of resources. The objective of this study was to develop a mobile application that uses an image classification model to detect banana leaf diseases. To achieve this, a literature review was carried out to identify the most common pests. Images of these pests were then collected to train the image classification model. The trained model was integrated into a native Android mobile application, which was then tested for usability. The results of the literature review identified six pests as the most common pests in banana crops. A total of 2856 images of these pests were collected for training the model, which demonstrated 98% accuracy in pest identification. Therefore, the developed mobile application is a promising solution, as confirmed by usability tests conducted by experts in the field.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Communication and Applied Technologies 2025 (ICOMTA 2025)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
22 October 2025
ISBN
978-94-6463-868-4
ISSN
2667-128X
DOI
10.2991/978-94-6463-868-4_2How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ariosto Vicuña Pino
AU  - Jessica Ponce Ordoñez
AU  - Bryan Steven Cortez Chichande
AU  - Jaime Mora Vera
PY  - 2025
DA  - 2025/10/22
TI  - Detecting Pests Affecting the AAB Muse Using an Image-recognising Mobile Application
BT  - Proceedings of the International Conference on Communication and Applied Technologies 2025 (ICOMTA 2025)
PB  - Atlantis Press
SP  - 4
EP  - 13
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-868-4_2
DO  - 10.2991/978-94-6463-868-4_2
ID  - Pino2025
ER  -