VLM-MCPDD: An Interpretable Vision Language Model for Multi-Crop Pests and Disease Diagnosis

Deep convolutional neural networks have made substantial progress in automated crop disease diagnosis. However, their practical application remains constrained by limited interpretability and insufficient structured reasoning, as these models largely operate as black boxes. Although they are effective in extracting visual features, they often fail to provide semantically grounded explanations, which may reduce their reliability in complex and open agricultural environments. To address these issues, this study constructs a Vision Language Model for Multi-Crop Pest and Disease Diagnosis (VLM-MCPDD). Specifically, the LLaVA-1.5 model is fine-tuned using low-rank adaptation (LoRA) to better align visual symptom representations with domain-specific agricultural knowledge. In addition, a Pests and Diseases Semantic Dataset (PDSD) is constructed to support multimodal learning. Based on PDSD, a chain-of-thought (CoT) mechanism is introduced to simulate the diagnostic workflow of agronomists, covering symptom observation, causal analysis, and final decision-making. The experimental results show that compared with comparative models such as Swin Transformer and ConvNeXt, VLM-MCPDD performs better in overall performance and can provide some reference for disease and pest diagnosis in intelligent agriculture.