Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914967766171648 |
|---|---|
| author | Singh, Namita Wang'ombe, Jacqueline Okanga, Nereah Zelenska, Tetyana Repishti, Jona K, Jayasankar G Mishra, Sanjeev Manokaran, Rajsekar Singh, Vineet Rafiq, Mohammed Irfan Gandhi, Rikin Nambi, Akshay |
| author_facet | Singh, Namita Wang'ombe, Jacqueline Okanga, Nereah Zelenska, Tetyana Repishti, Jona K, Jayasankar G Mishra, Sanjeev Manokaran, Rajsekar Singh, Vineet Rafiq, Mohammed Irfan Gandhi, Rikin Nambi, Akshay |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers Singh, Namita Wang'ombe, Jacqueline Okanga, Nereah Zelenska, Tetyana Repishti, Jona K, Jayasankar G Mishra, Sanjeev Manokaran, Rajsekar Singh, Vineet Rafiq, Mohammed Irfan Gandhi, Rikin Nambi, Akshay Emerging Technologies Artificial Intelligence Human-Computer Interaction 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. |
| title | Farmer.Chat: Scaling AI-Powered Agricultural Services for Smallholder Farmers |
| topic | Emerging Technologies Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.08916 |