A systematic review of orchid classification using convolutional neural networks: viable architectures for Moyobamba
DOI:
https://doi.org/10.36825/RITI.14.34.004Keywords:
Convolutional Neural Networks, Image Classification, Orchids, Systematic Review, BiodiversityAbstract
This study addresses the limited identification of orchid species in high-biodiversity environments, particularly in Moyobamba, where this issue hinders their conservation and use in tourism and educational contexts; therefore, the objective is to analyze the state of the art in automatic orchid classification using Convolutional Neural Networks (CNNs). The research was conducted through a systematic literature review based on the Kitchenham systematic review methodology, structured into planning, conducting, and reporting phases, applying inclusion and exclusion criteria to publications from 2024 to 2026 (with a search cut-off date of January 2026), resulting in 22 primary studies out of 371 initial records. The results show a predominance of architectures such as ResNet, DenseNet, and MobileNet, as well as the frequent use of techniques such as Transfer Learning and Data Augmentation, which significantly improve model performance. Limitations related to the scarcity of specific orchid datasets and their implementation in real-world environments were identified. It is concluded that CNNs represent a promising tool for automatic orchid identification, with potential applications in tropical biodiversity contexts such as the Alto Mayo Protected Forest.
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