A Two-Stage Multitask Vision-Language Framework for Explainable Crop Disease Visual Question Answering

Visual question answering (VQA) for crop disease analysis requires accurate visual understanding and reliable language generation. In this work, we present a lightweight and explainable vision-language framework for crop and disease identification from leaf images. The proposed approach integrates a Swin Transformer vision encoder with sequence-to-sequence language decoders. The vision encoder is first trained in a multitask setup for both plant and disease classification, and then frozen while the text decoders are trained, forming a two-stage training strategy that enhances visual representation learning and cross-modal alignment. We evaluate the model on the large-scale Crop Disease Domain Multimodal (CDDM) dataset using both classification and natural language generation metrics. Experimental results demonstrate near-perfect recognition performance, achieving 99.94% plant classification accuracy and 99.06% disease classification accuracy, along with strong BLEU, ROUGE and BERTScore results. Without fine-tuning, the model further generalizes well to the external PlantVillageVQA benchmark, achieving 83.18% micro accuracy in the VQA task. Our lightweight design outperforms larger vision-language baselines while using significantly fewer parameters. Explainability is assessed through Grad-CAM and token-level attribution, providing interpretable visual and textual evidence for predictions. Qualitative results demonstrate robust performance under diverse user-driven queries, highlighting the effectiveness of task-specific visual pretraining and the two-stage training methodology for crop disease visual question answering. An interactive demo of the proposed Swin-T5 model is publicly available as a Gradio-based application at https://huggingface.co/spaces/Zahid16/PlantDiseaseVQAwithSwinT5 for community use.