KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913577743417344 |
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| author | Rehman, Mohammad Zia Ur Raghuvanshi, Devraj Kumar, Nagendra |
| author_facet | Rehman, Mohammad Zia Ur Raghuvanshi, Devraj Kumar, Nagendra |
| contents | Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust query-response framework for the agriculture sector. KisanQRS integrates semantic and lexical similarities of farmers queries and employs a rapid threshold-based clustering method. The clustering algorithm is based on a linear search technique to iterate through all queries and organize them into clusters according to their similarity. For query mapping, LSTM is found to be the optimal method. Our proposed answer retrieval method clusters candidate answers for a crop, ranks these answer clusters based on the number of answers in a cluster, and selects the leader of each cluster. The dataset used in our analysis consists of a subset of 34 million call logs from the Kisan Call Centre (KCC), operated under the Government of India. We evaluated the performance of the query mapping module on the data of five major states of India with 3,00,000 samples and the quantifiable outcomes demonstrate that KisanQRS significantly outperforms traditional techniques by achieving 96.58% top F1-score for a state. The answer retrieval module is evaluated on 10,000 samples and it achieves a competitive NDCG score of 96.20%. KisanQRS is useful in enabling farmers to make informed decisions about their farming practices by providing quick and pertinent responses to their queries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making Rehman, Mohammad Zia Ur Raghuvanshi, Devraj Kumar, Nagendra Information Retrieval Artificial Intelligence Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust query-response framework for the agriculture sector. KisanQRS integrates semantic and lexical similarities of farmers queries and employs a rapid threshold-based clustering method. The clustering algorithm is based on a linear search technique to iterate through all queries and organize them into clusters according to their similarity. For query mapping, LSTM is found to be the optimal method. Our proposed answer retrieval method clusters candidate answers for a crop, ranks these answer clusters based on the number of answers in a cluster, and selects the leader of each cluster. The dataset used in our analysis consists of a subset of 34 million call logs from the Kisan Call Centre (KCC), operated under the Government of India. We evaluated the performance of the query mapping module on the data of five major states of India with 3,00,000 samples and the quantifiable outcomes demonstrate that KisanQRS significantly outperforms traditional techniques by achieving 96.58% top F1-score for a state. The answer retrieval module is evaluated on 10,000 samples and it achieves a competitive NDCG score of 96.20%. KisanQRS is useful in enabling farmers to make informed decisions about their farming practices by providing quick and pertinent responses to their queries. |
| title | KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2411.08883 |