Few-Shot Based Crop Weather Damage Discrimination System

This study introduces a model utilizing Few-shot Learning to effectively classify and discern weather-induced damages in crops, focusing on cold and heat damages affecting peaches, apples, and pears. It addresses the challenge of limited data availability in agriculture by leveraging Few-shot Learning, offering a promising solution for data scarcity issues. The model demonstrates robust classification capabilities under constrained data conditions, highlighting the potential of AI and machine learning technologies to tackle significant challenges in modern agriculture related to weather damage. The research suggests avenues for future work, including model performance enhancement, integration with real-time monitoring systems, and broader application across various crops and weather conditions, aiming to contribute to sustainable agriculture and food security.