A deep learning based approach for classifying the maturity of cashew apples
Publication date
2025-06-25
Document type
Forschungsartikel
Author
Organisational unit
Publisher
PLOS
Series or journal
PLOS ONE
ISSN
Periodical volume
2025
Article ID
e0326103
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
Over 95% of cashew apples are left to waste and rot on the ground. However, both cashew nuts and the often overlooked cashew apples possess significant nutritional and economic value. The cashew apple constitutes the major part (90%) of the cashew fruit, with the nut forming a modest portion (10%). Cashew nuts can be harvested and processed even after lying on the ground, but cashew apples are more delicate. Assessing the maturity status of these apples still requires human visual observation due to the challenges posed by their moisture content. Timely harvesting is crucial, as the pseudofruit is prone to microbial infections upon hitting the ground, making the process time- and labor-intensive. In this study, a Deep Learning based image classification model is presented, which can be used to automatically identify mature cashew apples. The model achieved an accuracy of 95.58% in classifying the cashew apples (immature vs. mature). Overall, the results highlight the potential of Deep Learning models for the classification of cashew apples and other fruits for precision agriculture purposes. This approach could enhance the harvesting process by enabling the utilization of the entire fruit and reducing the need for manual labor, thereby unlocking the full economic potential of the cashew tree.
Description
This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
This publication has been funded by the Open-Access-Publication-Fund of the Helmut-Schmidt-University/University of the Federal Armed Forces Hamburg.
Version
Published version
Access right on openHSU
Metadata only access