Meta-learning baselines and database for few-shot classification in agriculture

Learning from a few samples to automatically recognize the pests or plants is an attractive and promising study with a low cost of data to protect the agricultural yield and quality. Although there have been a handful of efforts on the few-shot classification in agriculture, none of them involves the task-driven meta-learning paradigm. This study is the first work of task-driven meta-learning few-shot classification in the field of agriculture to our best of knowledge. Specifically, we collected samples from publicly available resources to assemble a comprehensive dataset for the few-shot classification, covering both pests and plants to analyze the single domain or cross-domain. Then, we performed 36 groups of comparison experiments to establish the baselines of testing accu-racy. Further, we summarized and explained the effect laws of factors on the few-shot performance, such as N-way, K-shot, and domain shift. In summary, this work can be regarded as a significant reference and the benchmark comparison for the follow-up studies of few-shot learning tasks in the agricultural field.

Automated summary

This study is the first work of task-driven meta-learning few-shot classification in the field of agriculture, providing a significant reference and benchmark comparison for follow-up studies in the agricultural field by assembling a comprehensive dataset and conducting multiple comparison experiments.