A new study introduces a zero-shot learning (ZSL) framework designed for maize cob phenotyping, offering a powerful approach to extract geometric traits and estimate crop yields in both controlled laboratory environments and real-world field conditions. Unlike traditional machine learning models that require extensive labeled training data for each new task, this ZSL framework can generalize to unseen phenotypic variations without retraining. By leveraging semantic knowledge and attribute-based representations, the system identifies key features such as cob length, diameter, kernel count, and compactness, even when applied to new maize varieties or diverse growing conditions. The framework relies on pre-trained vision models and semantic embeddings to bridge the gap between visual data and descriptive traits, enabling accurate predictions based on learned relationships rather than direct examples. This capability significantly reduces the dependency on large annotated datasets, which are often time-consuming and costly to produce in agricultural research. The approach has demonstrated strong performance across multiple environments, showing robustness to variations in lighting, background, and plant maturity. Researchers believe this advancement could accelerate breeding programs, support precision agriculture, and improve yield forecasting by enabling rapid, scalable phenotyping without the need for retraining. The framework represents a major step forward in making AI-driven plant phenomics more accessible, efficient, and adaptable to real-world agricultural challenges.