Mobile Application for Pine Variety Classification in an Oxapampa Nursery Using CNN

Castillo R.C.C.; Asto A.M.G.L.; Alarcon J.S.D. · Proceedings of the 2024 IEEE 31st International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024 (2024) · DOI: 10.1109/intercon63140.2024.10833512

Resumen

Peru has large expanses of reforested hectares along its coast, with pine being one of the two most commonly used species for this practice. These plants originate from forest nurseries, which are centers for the reproduction and care of plants in their early stages of life before being transplanted to the field. A crucial stage in pine production is the classification of seeds by variety, which is fundamental for plant development. This influences the planting destination, subsequent care, and the transplant environment. In this article, we present a mobile application (app) for classifying pine seeds by variety. The app uses a model based on Convolutional Neural Networks (CNN) designed to distinguish and predict among the Tecunumanii, Oocarpa, and Pseudostrobus pine varieties. The mobile application was built in three phases: Data Selection, Model Training, and Development and Implementation. The model achieved an accuracy of 90.32% in classifying pine seeds, demonstrating effectiveness and potential as a useful tool in managing the seed classification process in forest nurseries.

Instituciones: Universidad Peruana de Ciencias Aplicadas