Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria
The limited number of agricultural extension agents (EAs) in sub-Saharan Africa limits farmers’ access to extension services. Artificial intelligence (AI) assistants could potentially aid in providing answers to farmers’ questions. The objective of this study was to evaluate the ability of an AI cha...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/139382 |
| _version_ | 1855526324462944256 |
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| author | Ibrahim, Ali Senthilkumar, Kalimuthu Saito, Kazuki |
| author_browse | Ibrahim, Ali Saito, Kazuki Senthilkumar, Kalimuthu |
| author_facet | Ibrahim, Ali Senthilkumar, Kalimuthu Saito, Kazuki |
| author_sort | Ibrahim, Ali |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The limited number of agricultural extension agents (EAs) in sub-Saharan Africa limits farmers’ access to extension services. Artificial intelligence (AI) assistants could potentially aid in providing answers to farmers’ questions. The objective of this study was to evaluate the ability of an AI chatbot assistant (ChatGPT) to provide quality responses to farmers’ questions. We compiled a list of 32 questions related to irrigated rice cultivation from farmers in Kano State, Nigeria. Six EAs from the state were randomly selected to answer these questions. Their answers, along with those of ChatGPT, were assessed by four evaluators in terms of quality and local relevancy. Overall, chatbot responses were rated significantly higher quality than EAs’ responses. Chatbot responses received the best score nearly six times as often as the EAs’ (40% vs. 7%). The evaluators preferred chatbot responses to EAs in 78% of cases. The topics for which the chatbot responses received poorer scores than those by EAs included planting time, seed rate, and fertilizer application rate and timing. In conclusion, while the chatbot could offer an alternative source for providing agricultural advisory services to farmers, incorporating site-specific input rate-and-timing agronomic practices into AI assistants is critical for their direct use by farmers. |
| format | Journal Article |
| id | CGSpace139382 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1393822025-11-13T10:38:45Z Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria Ibrahim, Ali Senthilkumar, Kalimuthu Saito, Kazuki extension programmes artificial intelligence agricultural extension farmers irrigated rice cultivation The limited number of agricultural extension agents (EAs) in sub-Saharan Africa limits farmers’ access to extension services. Artificial intelligence (AI) assistants could potentially aid in providing answers to farmers’ questions. The objective of this study was to evaluate the ability of an AI chatbot assistant (ChatGPT) to provide quality responses to farmers’ questions. We compiled a list of 32 questions related to irrigated rice cultivation from farmers in Kano State, Nigeria. Six EAs from the state were randomly selected to answer these questions. Their answers, along with those of ChatGPT, were assessed by four evaluators in terms of quality and local relevancy. Overall, chatbot responses were rated significantly higher quality than EAs’ responses. Chatbot responses received the best score nearly six times as often as the EAs’ (40% vs. 7%). The evaluators preferred chatbot responses to EAs in 78% of cases. The topics for which the chatbot responses received poorer scores than those by EAs included planting time, seed rate, and fertilizer application rate and timing. In conclusion, while the chatbot could offer an alternative source for providing agricultural advisory services to farmers, incorporating site-specific input rate-and-timing agronomic practices into AI assistants is critical for their direct use by farmers. 2024-02-10 2024-02-14T16:45:55Z 2024-02-14T16:45:55Z Journal Article https://hdl.handle.net/10568/139382 en Open Access application/pdf Springer Ibrahim, Ali, Kalimuthu Senthilkumar, and Kazuki Saito. 2024. Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria. Scientific Reports 14(1): 3407. https://doi.org/10.1038/s41598-024-53916-1 |
| spellingShingle | extension programmes artificial intelligence agricultural extension farmers irrigated rice cultivation Ibrahim, Ali Senthilkumar, Kalimuthu Saito, Kazuki Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria |
| title | Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria |
| title_full | Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria |
| title_fullStr | Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria |
| title_full_unstemmed | Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria |
| title_short | Evaluating responses by ChatGPT to farmers’ questions on irrigated lowland rice cultivation in Nigeria |
| title_sort | evaluating responses by chatgpt to farmers questions on irrigated lowland rice cultivation in nigeria |
| topic | extension programmes artificial intelligence agricultural extension farmers irrigated rice cultivation |
| url | https://hdl.handle.net/10568/139382 |
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