# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 1 Introduction  
Agriculture is the foundation of economic development and plays an important role in social stability. With the continuous development of modern agricultural technology, the process of agricultural production has produced a large amount of agricultural image and text data, which contain a variety of information and are significant for improving the efficiency of agricultural production and promoting sustainable development. The traditional methods of agricultural data analysis appear to be inadequate in facing these complex data, unable to efficiently and accurately extract valuable information; hence, smart agriculture has become the trend in future agriculture [1, 2]. Therefore, how to quickly and accurately analyze data in different modalities such as agricultural images and texts and mine useful information to assist people in making decisions is one of the current hot topics in agricultural information research. Recently, with the developments of artificial intelligence technology, deep learning has been widely used to analyze complex data in agriculture, providing powerful technical support for the intelligence of agricultural production. Among them, large language models (LLMs) [3, 4, 5] have obtained outstanding achievements in the field of natural language processing, providing a new viewpoint and method for analyzing agricultural text data. LLMs are usually built on the transformer [6] architecture and then learn therules and features through a large amount of text corpus. LLMs have shown quite a high level of capability in tasks such as natural language understanding and text generation. For example, the GPT series [7; 8; 9], ChatGLM [10], and large language model Meta AI (LLAMA) [11] and others have excellent performance in inference and analysis tasks, and have been applied in various fields such as medicine [12; 13], education [14], finance [15; 16], and law [17; 18]. However, large language models mainly focus on analyzing text data and have a significant shortcoming in analyzing visual data, i.e., images. In contrast, visual language large models are able to deeply analyze both image and text data using the powerful cross-modal understanding ability [19], which can be an effective way to solve this shortcoming. They offer a new direction for the analysis of different modal data in the field of agriculture and provide a more comprehensive solution for the understanding and application of agricultural knowledge. Most of the current large visual language models like ViLT [20], Qwen-VL [21], and VisCPM [22] learn from a huge amount of image-text pair data, achieving the analysis and recognition of target objects in images, as well as the extraction and generation of important information in text, and learning the correlation and matching between images and text simultaneously. However, although these models have presented excellent capabilities in general fields, when they are applied to agricultural knowledge understanding, especially in essential tasks such as crop disease recognition and growth stage judgment, their performance is often unsatisfactory due to the lack of in-depth understanding of specific agricultural knowledge, which creates certain errors and limitations. In order to reduce the impact of these problems, we propose a method, AgriVLM, to extract the features of agricultural images and text data through a visual language large model. AgriVLM uses Vision transformer as image encoder to extract image features, then employs Q-Former to connect the image encoder and pre-trained language model. Q-Former receives the text data and the output of the image encoder, combines them with query vectors to learn visual features related to the text, and exports a visual representation that the LLM can understand. Moreover, we applied Low-Rank adaptive (LoRA) to fine-tune the visual language large model through a lot of preprocessing agricultural data to achieve feature alignment.