PlantExpertVQA, a new visual question answering dataset for plant disease diagnosis, is introduced to address gaps in existing agricultural AI frameworks. Plant pests and diseases cause the loss of as much as 30% of global food crop yields annually, yet most current machine learning systems focus only on classification and lack interactive reasoning. The dataset is built on 45 open-sourced datasets with hierarchical annotations and contains 765,186 question-answer pairs grounded over 150,841 images across 38 unique crop species and 89 disease conditions, covering nine question categories under three levels of cognitive complexity. Questions were naturally phrased and tailored through expert review, combining automated template-based generation with multistage linguistic re-engineering and iterative botanist review. The authors benchmarked the dataset by evaluating nine open-source vision-language models in a zero-shot setting.