Farmer-Bot: An Interactive Bot for Farmers

• URL: http://arxiv.org/abs/2204.07032v1
• Date: Thu, 7 Apr 2022 17:52:21 GMT
• Title: Farmer-Bot: An Interactive Bot for Farmers
• Authors: Narayana Darapaneni, Rajiv Tiwari, Anwesh Reddy Paduri, Suman Saurav, Rohit Chaoji, and Sohil
• Abstract summary: We will build an NLP model by getting the semantic similarity of the queries made by farmers in the past and use it to automatically answer future queries. We will attempt to make a WhatsApp based chat-bot to easily communicate with farmers using RASA as a tool.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Indian Agricultural sector generates huge employment accounting for over 54% of countrys workforce. Its overall stand in GDP is close to 14%. However, this sector has been plagued by knowledge and infrastructure deficit, especially in the rural sectors. Like other sectors, the Indian Agricultural sector has seen rapid digitization with use of technology and Kisan Call Center (KCC) is one such example. It is a Government of India initiative launched on 21st January 2004 which is a synthesis of two hitherto separate sectors the Information Technology and Agriculture sector. However, studies have shown to have constrains to KCC beneficiaries, especially in light of network congestion and incomplete knowledge of the call center representatives. With the advent of new technologies, like first-generation SMS based and next-generation social media tools like WhatsApp, farmers in India are digitally more connected to the agricultural information services. Previous studies have shown that the KCC dataset can be used as a viable alternative for Chat-bot. We will base our study with the available KCC dataset to build an NLP model by getting the semantic similarity of the queries made by farmers in the past and use it to automatically answer future queries. We will attempt to make a WhatsApp based chat-bot to easily communicate with farmers using RASA as a tool.

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)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• 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)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• Future Computer Systems and Networking Research in the Netherlands: A Manifesto [137.47124933818066]
  We draw attention to CompSys as a vital part of ICT. Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
  arXiv  Detail & Related papers  (2022-05-26T11:02:29Z)
• 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)
• Diagnosing Web Data of ICTs to Provide Focused Assistance in Agricultural Adoptions [9.621466132073175]
  We focus on the web infrastructure of one such ICT - Digital Green that started in 2008. Our research finds that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller villages. We model the adoption of practices from a video as a prediction problem to identify and assist farmers who might face challenges in adoption in each of the five states.
  arXiv  Detail & Related papers  (2021-10-29T19:24:58Z)
• Transforming India's Agricultural Sector using Ontology-based Tantra Framework [0.0]
  India is one of the largest producers of food grains in the world. Keeping farmers happy is particularly important in India as farmers a large vote bank which politicians dare not disappoint. Governments need to balance the interest of farmers with consumers, intermediaries and society at large.
  arXiv  Detail & Related papers  (2021-01-26T04:05:14Z)
• Adoption of ICT innovations in the agriculture sector in Africa: A Systematic Literature Review [3.0969191504482243]
  This study aims to explore ICT innovations in the agriculture sector of Africa. The main ICT technologies adopted are text and voice-based services targeting mobile phones. The adoption of the services is constrained by poor technological infrastructure, inappropriate ICT policies and low capacity levels of users.
  arXiv  Detail & Related papers  (2020-06-24T16:03:27Z)
• Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks [77.63950365605845]
  Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
  arXiv  Detail & Related papers  (2020-04-11T19:49:09Z)
• 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.