## 2 Materials and Methods  
This paper proposes a novel method for extracting knowledge about fruit tree diseases--K-Extract (\"K\" for Knowledge), based on large language models and prompt engineering. The method aims to achieve rapid knowledge extraction from the abstracts of scientific research papers. The implementation process is as follows.  
Step 1: Construction of Knowledge Classification. Based on survey research, a literature review, and expert validation, a classification system for fruit tree disease knowledge is constructed, encompassing a total of eight categories of questions.  
Step 2: Initial Prompt Construction. Based on the fruit tree disease knowledge classification system, an initial set of prompt questions is developed.  
Step 3: Abstract Data Preparation. Design search terms to retrieve research papers related to fruit tree disease knowledge from the web of science (WoS) database, sort them by relevance, and select the top 100. Download the research papers, extract the abstract data, modify the format of the abstract data (JSON), clean the abstract data, and store them as an abstract data file.  
Step 4: Selection of Large Language Models. Select the five most popular large language models in the Chinese market for experimentation, namely DeepSeek, doubao, Kimi, wanzhi, and qwen.  
Step 5: Prompt Optimization. Randomly select sample abstract data and use the five large language models to pose questions to the initial set of prompt questions individually, adjust the prompt questions based on the answers, reask the questions, and make further adjustments until the output results from all five large language models are consistently correct. At this point, an optimized set of fruit tree disease knowledge extraction questions is obtained, categorized into eight classes.  
Step 6: Knowledge Extraction. Write Python code to call the API interfaces of the five large language models in an automated manner, invoking the abstract data file (100 abstracts) to generate a batch of prompt questions (800 questions), ask each question individually, and record the answers (4000 answers).  
Step 7: Preparation of Expected Output Results (Mannual Answers). Manually answer the questions for 100 abstracts \(\times\) 8 questions. The manual answers are extracted by experts in the field of fruit tree plant protection according to the answer rules and are reviewed and verified by another expert in the same field. The final archived records serve as the correct knowledge.  
Step 8: Model Evaluation. Compare the manual answer set with the answers from the large language models to evaluate the performance (accuracy, precision) of each model and draw conclusions.  
Step 9: Knowledge Base Construction. Constructed a knowledge base for fruit tree diseases based on the output results from the large language models.  
Figure 1 illustrates the complete workflow of the K-Extract method. Results indicate that this method achieves high accuracy and precision. The primary users of the K-Extract method are practitioners in the fruit tree industry. This method can provide a tool that can be directly applied or reused by fruit farmers, fruit tree knowledge communicators, agricultural researchers, and other agricultural industry practitioners. For groups with  
lower levels of information technology skills, such as fruit farmers, they can refer to the optimized set of fruit tree disease knowledge extraction questions proposed by this method, replace the content of the abstract data, and pose questions to large language models to rapidly extract relevant knowledge about fruit tree diseases from the abstract data. For fruit tree knowledge communicators, agricultural researchers, and others facing fields with a multitude of literature and high demands for rapid knowledge updates, such as fruit tree pests, fruit tree cultivation, and grain crop cultivation, this method can be reapplied by replacing search terms and reoptimizing prompt questions.