A Domain-Adapted Lightweight Ensemble for Resource-Efficient Few-Shot Plant Disease Classification

This article presents a research paper focused on a specific application of machine learning: classifying plant diseases with limited data (few-shot learning) while being mindful of computational resources. The approach involves a domain-adapted lightweight ensemble, suggesting the use of multiple models tailored to the specific data and designed to be computationally efficient. The focus on resource efficiency is particularly relevant given the potential deployment of such models in environments with limited computational power.

• •Focuses on few-shot learning for plant disease classification.
• •Employs a domain-adapted lightweight ensemble.
• •Prioritizes resource efficiency for practical deployment.