Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers
- URL: http://arxiv.org/abs/2409.08916v2
- Date: Tue, 8 Oct 2024 06:03:41 GMT
- Title: Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers
- Authors: Namita Singh, Jacqueline Wang'ombe, Nereah Okanga, Tetyana Zelenska, Jona Repishti, Jayasankar G K, Sanjeev Mishra, Rajsekar Manokaran, Vineet Singh, Mohammed Irfan Rafiq, Rikin Gandhi, Akshay Nambi,
- Abstract summary: Small and medium-sized agricultural holders face challenges like limited access to localized, timely information, impacting productivity and sustainability.
Traditional extension services, which rely on in-person agents, struggle with scalability and timely delivery.
We introduce FarmerChat, a generative AI-powered chatbots designed to address these issues.
Deployed in four countries, FarmerChat has engaged over 15,000 farmers and answered over 300,000 queries.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Small and medium-sized agricultural holders face challenges like limited access to localized, timely information, impacting productivity and sustainability. Traditional extension services, which rely on in-person agents, struggle with scalability and timely delivery, especially in remote areas. We introduce FarmerChat, a generative AI-powered chatbot designed to address these issues. Leveraging Generative AI, FarmerChat offers personalized, reliable, and contextually relevant advice, overcoming limitations of previous chatbots in deterministic dialogue flows, language support, and unstructured data processing. Deployed in four countries, FarmerChat has engaged over 15,000 farmers and answered over 300,000 queries. This paper highlights how FarmerChat's innovative use of GenAI enhances agricultural service scalability and effectiveness. Our evaluation, combining quantitative analysis and qualitative insights, highlights FarmerChat's effectiveness in improving farming practices, enhancing trust, response quality, and user engagement.
Related papers
- AgriLLM: Harnessing Transformers for Farmer Queries [2.8592691160719554]
This work explores the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers.
Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu.
arXiv Detail & Related papers (2024-06-21T07:37:41Z) - AutoConv: Automatically Generating Information-seeking Conversations
with Large Language Models [74.10293412011455]
We propose AutoConv for synthetic conversation generation.
Specifically, we formulate the conversation generation problem as a language modeling task.
We finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process.
arXiv Detail & Related papers (2023-08-12T08:52:40Z) - Chatbot Application to Support Smart Agriculture in Thailand [0.3523208537466128]
In the agriculture sector, the existing smart agriculture systems just use data from sensing and internet of things (IoT) technologies.
To enhance this, the application can be an assistant to farmers to provide crop cultivation knowledge.
It consists of five main functions (start/stop menu, main page, drip irri gation page, mist irrigation page, and monitor page)
Farmers are very satisfied with the application, scoring a 96% satisfaction score.
arXiv Detail & Related papers (2023-07-31T11:42:44Z) - ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic
Agricultural Text Classification [8.18726897455402]
It is urgent to explore effective text classification techniques for users to access the required agricultural knowledge with high efficiency.
Mainstream deep learning approaches employing fine-tuning strategies on pre-trained language models (PLMs) have demonstrated remarkable performance gains over the past few years.
In this work, we investigate and explore the capability and utilization of ChatGPT applying to the agricultural informatization field.
arXiv Detail & Related papers (2023-05-24T11:06:23Z) - 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) - Knowledge-Grounded Conversational Data Augmentation with Generative
Conversational Networks [76.11480953550013]
We take a step towards automatically generating conversational data using Generative Conversational Networks.
We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset.
arXiv Detail & Related papers (2022-07-22T22:37:14Z) - LSTM-RASA Based Agri Farm Assistant for Farmers [1.4777718769290527]
This project aims to implement a closed domain ChatBot for the field of Agriculture.
Farmers Assistant is based on RASA Open Source Framework.
It identifies the user entity from utterances and retrieves the remedy from the database.
arXiv Detail & Related papers (2022-04-07T11:01:54Z) - Training Conversational Agents with Generative Conversational Networks [74.9941330874663]
We use Generative Conversational Networks to automatically generate data and train social conversational agents.
We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.
arXiv Detail & Related papers (2021-10-15T21:46:39Z) - 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) - 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.