Template-based Chatbot for Agriculture Related FAQs
- URL: http://arxiv.org/abs/2107.12595v1
- Date: Tue, 27 Jul 2021 04:46:29 GMT
- Title: Template-based Chatbot for Agriculture Related FAQs
- Authors: Daping Zhang, Xin Chen, Yujia Zhang, Shihan Qin
- Abstract summary: Agriculture is the fundamental industry of the society, which is the basis of food supply and an important source of employment and GDP increase.
To address this problem, we design a robot to answer frequently asked questions in the Agriculture field.
- Score: 5.869240004408606
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Agriculture is the fundamental industry of the society, which is the basis of
food supply and an important source of employment and GDP increase. However,
the insufficient expert can not fulfill the demand of farmers. To address this
problem, we design a chatbot to answer frequently asked questions in the
Agriculture field. Template-based questions will be answered by AIML while LSA
is used for other service-based questions. This chatbot will assist farmers by
dealing with industry problems conveniently and efficiently.
Related papers
- Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers [0.0]
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.
arXiv Detail & Related papers (2024-09-13T15:31:33Z) - Harnessing Large Vision and Language Models in Agriculture: A Review [3.6673562709926664]
Large models can help farmers improve production efficiency and harvest by detecting a series of agricultural production tasks.
After gaining a deeper understanding of multimodal large language models (MLLM), it can be recognized that problems such as agricultural image processing, agricultural question answering systems, and agricultural machine automation can all be solved by large models.
arXiv Detail & Related papers (2024-07-29T03:47:54Z) - 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) - 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) - Chatbots put to the test in math and logic problems: A preliminary
comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard [68.8204255655161]
We use 30 questions that are clear, without any ambiguities, fully described with plain text only, and have a unique, well defined correct answer.
The answers are recorded and discussed, highlighting their strengths and weaknesses.
It was found that ChatGPT-4 outperforms ChatGPT-3.5 in both sets of questions.
arXiv Detail & Related papers (2023-05-30T11:18:05Z) - 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) - Search-Engine-augmented Dialogue Response Generation with Cheaply
Supervised Query Production [98.98161995555485]
We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation.
As the core module, a query producer is used to generate queries from a dialogue context to interact with a search engine.
Experiments show that our query producer can achieve R@1 and R@5 rates of 62.4% and 74.8% for retrieving gold knowledge.
arXiv Detail & Related papers (2023-02-16T01:58:10Z) - 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) - A Dataset of Information-Seeking Questions and Answers Anchored in
Research Papers [66.11048565324468]
We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers.
Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text.
We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
arXiv Detail & Related papers (2021-05-07T00:12:34Z) - Understanding Unnatural Questions Improves Reasoning over Text [54.235828149899625]
Complex question answering (CQA) over raw text is a challenging task.
Learning an effective CQA model requires large amounts of human-annotated data.
We address the challenge of learning a high-quality programmer (parser) by projecting natural human-generated questions into unnatural machine-generated questions.
arXiv Detail & Related papers (2020-10-19T10:22:16Z) - 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.