CDIP-ChatGLM3: A dual-model approach integrating computer vision and language modeling for crop disease identification and prescription

Abstract

Deep learning (DL) models have shown exceptional accuracy in plant disease identification, yet their practical utility for farmers remains limited due to a lack of professional and actionable guidance. To bridge this gap, we developed CDIP-ChatGLM3, an innovative framework that synergizes a state-of-the-art DL-based computer vision model with a fine-tuned large language model (LLM), designed specifically for Crop Disease Identification and Prescription (CDIP). EfficientNet-B2, evaluated among 10 DL models across 48 diseases and 13 crops, achieved top performance with 97.97 % ± 0.16 % accuracy at a 95 % confidence level. Building on this, we fine-tuned the widely used ChatGLM3-6B LLM using Low-Rank Adaptation (LoRA) and Freeze-tuning, optimizing its ability to deliver precise disease management prescriptions. We compared two training strategies—multi-task learning (MTL) and Dual-stage Mixed Fine-Tuning (DMT)—using a different combination of domain-specific and general datasets. Freeze-tuning with DMT led to substantial performance gains, achieving a 33.16 % improvement in BLEU-4 and a 27.04 % increase in the Average ROUGE F-score, surpassing the original model and state-of-the-art competitors such as Qwen-max, Llama-3.1-405B-Instruct, and GPT-4o. The dual-model architecture of CDIP-ChatGLM3 leverages the complementary strengths of computer vision for image-based disease detection and LLMs for contextualized, domain-specific text generation, offering unmatched specialization, interpretability, and scalability. Unlike resource-intensive multimodal models that blend modalities, our dual-model approach maintains efficiency while achieving superior performance in both disease identification and actionable prescription generation.

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.