Can Vision-Language Models Overthrow Supervised Learning in Agriculture?

This ArXiv paper explores the potential of vision-language models for zero-shot image classification in agriculture, comparing them to established supervised methods. The study's findings will be crucial for understanding the feasibility of adopting these newer models in a practical agricultural setting.

• •Investigates the zero-shot capabilities of vision-language models.
• •Compares performance against supervised classification models.
• •Aims to evaluate the suitability for agricultural applications.