Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering
In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of pro...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/628 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589268467318784 |
|---|---|
| author | Yunqiao Fei Jingchao Fan Guomin Zhou |
| author_facet | Yunqiao Fei Jingchao Fan Guomin Zhou |
| author_sort | Yunqiao Fei |
| collection | DOAJ |
| description | In China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field. |
| format | Article |
| id | doaj-art-755a570e7842427a867d6c0ae31059b7 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-755a570e7842427a867d6c0ae31059b72025-01-24T13:20:10ZengMDPI AGApplied Sciences2076-34172025-01-0115262810.3390/app15020628Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt EngineeringYunqiao Fei0Jingchao Fan1Guomin Zhou2Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaNational Agricultural Science Data Center, Beijing 100081, ChinaIn China, fruit tree diseases are a significant threat to the development of the fruit tree industry, and knowledge about fruit tree diseases is the most needed professional knowledge for fruit farmers and other practitioners in the fruit tree industry. Research papers are the primary sources of professional knowledge that represent the cutting-edge progress in fruit disease research. Traditional knowledge engineering methods for knowledge acquisition require extensive and cumbersome preparatory work, and they demand a high level of professional background and information technology skills from the handlers. This paper, from the perspective of fruit tree industry knowledge dissemination, aims at users such as fruit farmers, fruit tree experts, fruit tree knowledge communicators, and information gatherers. It proposes a fast, cost-effective, and low-technical-barrier method for extracting fruit tree disease knowledge from research paper abstracts—K-Extract, based on large language models (LLMs) and prompt engineering. Under zero-shot conditions, K-Extract utilizes conversational LLMs to automate the extraction of fruit tree disease knowledge. The K-Extract method has constructed a comprehensive classification system for fruit tree diseases and, through a series of optimized prompt questions, effectively overcomes the deficiencies of LLM models in providing factual accuracy. This paper tests multiple LLM models available in the Chinese market, and the results show that K-Extract can seamlessly integrate with any conversational LLM model, with the DeepSeek model and the Kimi model performing particularly well. The experimental results indicate that LLM models have a high accuracy rate in handling judgment tasks and simple knowledge Q&A tasks. The K-Extract method is simple, efficient, and accurate, and can serve as a convenient tool for knowledge extraction in the agricultural field.https://www.mdpi.com/2076-3417/15/2/628research papersknowledge extractionlarge language modelsprompt engineeringfruit tree diseases |
| spellingShingle | Yunqiao Fei Jingchao Fan Guomin Zhou Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering Applied Sciences research papers knowledge extraction large language models prompt engineering fruit tree diseases |
| title | Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering |
| title_full | Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering |
| title_fullStr | Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering |
| title_full_unstemmed | Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering |
| title_short | Extracting Fruit Disease Knowledge from Research Papers Based on Large Language Models and Prompt Engineering |
| title_sort | extracting fruit disease knowledge from research papers based on large language models and prompt engineering |
| topic | research papers knowledge extraction large language models prompt engineering fruit tree diseases |
| url | https://www.mdpi.com/2076-3417/15/2/628 |
| work_keys_str_mv | AT yunqiaofei extractingfruitdiseaseknowledgefromresearchpapersbasedonlargelanguagemodelsandpromptengineering AT jingchaofan extractingfruitdiseaseknowledgefromresearchpapersbasedonlargelanguagemodelsandpromptengineering AT guominzhou extractingfruitdiseaseknowledgefromresearchpapersbasedonlargelanguagemodelsandpromptengineering |