Abstract: To address issues such as insufficient recognition accuracy and the lack of pest control recommendation generation in agricultural pest and disease identification,this paper proposes an automatic question-answering model that integrates computer vision techniques with instruction tuning strategies.An improved Vision Transformer(ViT) model is employed for classifying agricultural crop pest and disease images,incorporating an asymmetric convolution embedding module and a channel attention mechanism to enhance feature extraction capabilities and improve classification accuracy on large-scale datasets.Based on the classification results,LoRA(Low-Rank Adaptation) technology is applied to fine-tune the Baichuan large language model through instruction tuning,generating more precise and practical prevention and control recommendations,thereby enhancing the model's applicability in agricultural scenarios.The entire experiment is conducted on the Huawei MindSpore deep learning framework,leveraging the high-performance computing capabilities of the Ascend 910 NPU for efficient model training and inference.Experimental results demonstrate that combining the improved ViT model with the instruction fine-tuning strategy not only significantly improves classification accuracy but also generates highly actionable prevention and control recommendations.

Key words: Image classification, Agricultural pests and diseases, Large models, Instruction fine-tuning, Domestic AI framework