Emphasizing Data Quality for the Identification of Chili Varieties in the Context of Smart Agriculture
Wiwin Suwarningsih,
Rinda Kirana,
Purnomo H Khotimah
et al.
Abstract:Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture.
Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chi… Show more
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