# KisanQRS: A Deep Learning-based Automated Query-Response System for Agricultural Decision-Making

arXiv: https://arxiv.org/abs/2411.08883 · HTML: https://mlsanity.com/paper/2411.08883/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs](https://mlsanity.com/paper/2008.13546/) (2020)
- [FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance](https://mlsanity.com/paper/1905.02851/) (2019)
- [Efficiently Embedding Dynamic Knowledge Graphs](https://mlsanity.com/paper/1910.06708/) (2019)
- [Sentiment Dynamics in Social Media News Channels](https://mlsanity.com/paper/1908.08147/) (2019)

## Built on by (most cited first, 5 of 5)

- [A Context-aware Attention and Graph Neural Network-based Multimodal Framework for Misogyny Detection](https://mlsanity.com/paper/2508.09175/) (2025)
- [A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction](https://mlsanity.com/paper/2509.15966/) (2025)
- [Dynamic Orchestration of Multi-Agent System for Real-World Multi-Image Agricultural VQA](https://mlsanity.com/paper/2509.24350/) (2025)
- [An Adaptive Supervised Contrastive Learning Framework for Implicit Sexism Detection in Digital Social Networks](https://mlsanity.com/paper/2507.05271/) (2025)
- [Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection](https://mlsanity.com/paper/2610.17996/) (2026)

## Abstract (arXiv)

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.