The ability to recognize whether a plant is afflicted with a disease is of the utmost importance in the maintenance of food security on a global scale, the improvement of crop output, and the facilitation of agricultural methods that are sustainable. Due to the fact that conventional deep learning algorithms for the diagnosis of plant diseases rely primarily on big, labeled datasets, they are not as successful in real-world situations when annotated samples are limited or novel disease variants appear. This research suggests a brand-new zero-shot framework for the detection of plant diseases that is based on the Contrastive Language–Image Pre-training (CLIP) paradigm in order to solve these limitations. By utilizing the potent cross-modal learning capabilities of CLIP, the framework is able to correlate the visual characteristics of plant leaves with natural language descriptions of illnesses, which in turn facilitates precise classification without the need for labeled plant pathology datasets or task-specific training. In order to assess similarity within CLIP's joint embedding space, leaf pictures were coupled with prompt-engineered language descriptions of plant illnesses in this study. The model's zero-shot performance over a wide range of climatic circumstances and crop kinds was evaluated by running it on benchmark and real-world plant disease datasets. The findings reveal that the suggested CLIP-based technique is able to accomplish robust generalization, and it performs better than a number of standard supervised and transfer-learning models in situations when there is a limited amount of data. The framework demonstrates a significant capacity to recognize disease classifications that have not been observed before, manage fluctuations in lighting conditions, and adjust to a wide range of backdrops that are characteristic in agricultural settings. The ability of CLIP's zero-shot learning to decrease the need for huge annotated datasets, speed illness detection, and facilitate the creation of scalable digital farming systems is demonstrated in this work, which also emphasizes the promise of vision–language models in the field of agricultural artificial intelligence. By providing a more adaptable and effective approach to the identification of plant diseases at their earliest stages, the framework that has been suggested creates new opportunities for utilizing foundation models in precision agriculture