AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application
- URL: http://arxiv.org/abs/2503.04788v1
- Date: Fri, 28 Feb 2025 04:13:18 GMT
- Title: AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application
- Authors: Dinesh Jackson Samuel, Inna Skarga-Bandurova, David Sikolia, Muhammad Awais,
- Abstract summary: AgroLLM is designed to enhance knowledge-sharing and education in agriculture using Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework.<n>A comparative study of three advanced models was conducted to evaluate performance across four key agricultural domains.<n>ChatGPT-4o Mini with RAG achieved the highest accuracy at 93%.
- Score: 1.9643850583333375
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: AgroLLM is an AI-powered chatbot designed to enhance knowledge-sharing and education in agriculture using Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) framework. By using a comprehensive open-source agricultural database, AgroLLM provides accurate, contextually relevant responses while reducing incorrect information retrieval. The system utilizes the FAISS vector database for efficient similarity searches, ensuring rapid access to agricultural knowledge. A comparative study of three advanced models: Gemini 1.5 Flash, ChatGPT-4o Mini, and Mistral-7B-Instruct-v0.2 was conducted to evaluate performance across four key agricultural domains: Agriculture and Life Sciences, Agricultural Management, Agriculture and Forestry, and Agriculture Business. Key evaluation metrics included embedding quality, search efficiency, and response relevance. Results indicated that ChatGPT-4o Mini with RAG achieved the highest accuracy at 93%. Continuous feedback mechanisms enhance response quality, making AgroLLM a benchmark AI-driven educational tool for farmers, researchers, and professionals, promoting informed decision-making and improved agricultural practices.
Related papers
- Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases [49.782064512495495]
We construct the first multimodal instruction-following dataset in the agricultural domain.<n>This dataset covers over 221 types of pests and diseases with approximately 400,000 data entries.<n>We propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system.
arXiv Detail & Related papers (2024-12-03T04:34:23Z) - LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions [40.08908132533476]
The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices.
This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems.
arXiv Detail & Related papers (2024-09-17T13:55:44Z) - Enhancing Agricultural Machinery Management through Advanced LLM Integration [0.7366405857677226]
The integration of artificial intelligence into agricultural practices has the potential to revolutionize efficiency and sustainability in farming.
This paper introduces a novel approach that leverages large language models (LLMs), particularly GPT-4, to enhance decision-making processes in agricultural machinery management.
arXiv Detail & Related papers (2024-07-30T06:49:55Z) - Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues [6.0460261046732455]
Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored.
This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement.
arXiv Detail & Related papers (2024-05-23T17:53:31Z) - Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions.
Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
arXiv Detail & Related papers (2024-03-27T08:42:47Z) - GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using
Large Language Models [1.3999521658236698]
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding across various domains.
We present a comprehensive evaluation of popular LLMs, such as Llama 2 and GPT, on their ability to answer agriculture-related questions.
We selected agriculture exams and benchmark datasets from three of the largest agriculture producer countries: Brazil, India, and the USA.
arXiv Detail & Related papers (2023-10-10T00:39:04Z) - Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry.
We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery.
We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
arXiv Detail & Related papers (2023-05-03T05:16:54Z) - Affordable Artificial Intelligence -- Augmenting Farmer Knowledge with
AI [1.9992810351494297]
This article presents the AI technology for predicting micro-climate conditions on the farm.
This publication is the fifth in the E-agriculture in Action series, launched in 2016 and jointly produced by FAO and ITU.
It aims to raise awareness about existing AI applications in agriculture and to inspire stakeholders to develop and replicate the new ones.
arXiv Detail & Related papers (2023-03-04T02:29:52Z) - Learning from Data to Optimize Control in Precision Farming [77.34726150561087]
Special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming.
Satellite positioning and navigation followed by Internet-of-Things generate vast information that can be used to optimize farming processes in real-time.
arXiv Detail & Related papers (2020-07-07T12:44:17Z) - Crop Knowledge Discovery Based on Agricultural Big Data Integration [2.597676155371155]
Agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses.
We propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models.
arXiv Detail & Related papers (2020-03-11T00:13:17Z) - Data Warehouse and Decision Support on Integrated Crop Big Data [0.0]
We designed and implemented a continental level agricultural data warehouse (ADW)
ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility.
arXiv Detail & Related papers (2020-03-10T00:10:22Z) - Agriculture-Vision: A Large Aerial Image Database for Agricultural
Pattern Analysis [110.30849704592592]
We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel.
We annotate nine types of field anomaly patterns that are most important to farmers.
arXiv Detail & Related papers (2020-01-05T20:19:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.