Data-Driven Insights: A Critical Analysis of Farmer Call Centre Data Using Machine Learning Techniques

Data-Driven Insights: A Critical Analysis of Farmer Call Centre Data Using Machine Learning Techniques C. Kiruthiga K. Dharmarajan

The agricultural sector plays a crucial role in India's economy, society, and environment. Agriculture is the primary source of livelihood for a significant portion of the Indian population, employing over half of the country's workforce. It contributes substantially to the Gross Domestic Product (GDP) and remains a vital sector for rural development and poverty alleviation. Experts use different kinds of smart systems to figure out problems on farms and find possible solutions. The systems help the experts collect and analyze information regarding the issues farmers meet. This study aimed to investigate the query data from Kisan Call Centers (KCCs) from 2020 to 2023 to identify key issues, understand farmers' challenges, and provide data-driven policy and program development insights. Python was used for data processing, Power BI for visualization, and Machine learning algorithms and Natural Language Processing libraries for query analysis
03 23 2025 https://creativecommons.org/licenses/by/4.0 10.22399/ijcesen.1348 https://ijcesen.com/index.php/ijcesen/article/view/1348 https://ijcesen.com/index.php/ijcesen/article/download/1348/639 https://ijcesen.com/index.php/ijcesen/article/download/1348/639 10.17485/IJST/v16i19.94 Anima, P & Aneeshkumar, A. (2023). Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction. Indian Journal Of Science And Technology. 16. 1438-144, https://doi.org/ 10.17485/IJST/v16i19.94 10.1016/j.compag.2020.105448 Samarth Godara, Durga Toshniwal, (2020) Sequential pattern mining combined multi-criteria decision-making for farmers’ queries characterization, Computers and Electronics in Agriculture, 173; 105448 https://doi.org/10.1016/j.compag.2020.105448 10.26725/JEE.2017.3.29.5910-5916 Kavitha, S., & Anandaraja, N. (2018). Kisan Call Centre Services to the Farming Community: An Analysis. Journal of Extension Education, 29(3). https://doi.org/10.26725/JEE.2017.3.29.5910-5916 10.26725/JEE.2016.4.28.5753-5759 Koshy, S. M., & Kishore Kumar, N. (2017). Attitude of Farmers towards Kisan Call Centres. Journal of Extension Education, 28(4). https://doi.org/10.26725/JEE.2016.4.28.5753-5759 10.9734/ajaees/2024/v42i12347 D. P. Ramasamy, K. Geetha, V. Radhakrishnan, and Iniya V., (2024). Comprehensive Analysis of Kisan Call Centre in Tiruchirappalli District of Tamil Nadu, India, AJAEES, 42(1);62–67. 10.9734/jeai/2024/v46i22316 Yadav , V., R. K. Doharey, J. Vishwakarma, N. R. Meena, M. Kumar, and Y. Kumar. (2024). Cultivating Solutions: Farmer-Driven Constraints and Suggestions for Kisan Call Centre Advisories. Journal of Experimental Agriculture International, 46(2);141-6, https://doi:10.9734/jeai/2024/v46i22316 10.1016/j.compag.2022.107308 Samarth Godara, Durga Toshniwal, Rajender Parsad, Ram Swaroop Bana, Deepak Singh, Jatin Bedi, Abimanyu Jhajhria, Jai Prakash Singh Dabas, Sudeep Marwaha, (2022). AgriMine:A Deep Learning integrated Spatio-temporal analytics framework for diagnosing nationwide agricultural issues using farmers’ helpline data, Computers and Electronics in Agriculture, 201;107308, https://doi.org/10.1016/j.compag.2022.107308 10.1016/j.compag.2022.106875 Samarth Godara, Durga Toshniwal, (2022) Deep Learning-based query-count forecasting using farmers’ helpline data, Computers and Electronics in Agriculture,196;106875, https://doi.org/10.1016/j.compag.2022.106875 10.1109/ICACCI.2018.8554531 V. K. Viswanath, C. G. V. Madhuri, C. Raviteja, S. Saravanan and M. Venugopalan, (2018). Hadoop and Natural Language Processing Based Analysis on Kisan Call Center (KCC) Data," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 2018, pp. 1142-1151, https://doi: 10.1109/ICACCI.2018.8554531 10.1016/j.compag.2023.108180 Mohammad Zia Ur Rehman, Devraj Raghuvanshi, Nagendra Kumar, (2023) Kisan QRS: A deep learning-based automated query-response system for agricultural decision-making, Computers and Electronics in Agriculture,213;108180, https://doi.org/10.1016/j.compag.2023.108180 10.1109/ACCESS.2023.3339253 S. Godara, J. Bedi, R. Parsad, D. Singh, R. S. Bana and S. Marwaha, (2024). AgriResponse: A Real-Time Agricultural Query-Response Generation System for Assisting Nationwide Farmers, IEEE Access, 12;294-311, https://doi: 10.1109/ACCESS.2023.3339253. 10.26725/JEE.2016.4.28.5753-5759 Koshy, S. M., & Kishore Kumar, N. (2017). Attitude of Farmers towards Kisan Call Centres. Journal of Extension Education, 28(4). https://doi.org/10.26725/JEE.2016.4.28.5753-5759 10.26725/JEE.2017.3.29.5910-5916 Kavitha, S., & Anandaraja, N. (2018). Kisan Call Centre Services to the Farming Community: An Analysis. Journal of Extension Education, 29(3). https://doi.org/10.26725/JEE.2017.3.29.5910-5916 Goyal, Shashikant & Jirli, Basavaprabhu. (2021). Perceived Problems and Suggestions of Farmers regarding Kisan Call Centre. 10.20546/ijcmas.2019.808.346 Puja Sinha, Meera Kumari, Sandeep Kumar and Ramnath Kumar (2019). Ray Constraints in Pulse Cultivation Perceived by the Farmers of Tal Land in Patna District of Bihar, India Int. J. Curr.Microbiol.App.Sci. 8(8);2991-2997 DOI: https://doi.org/10.20546/ijcmas.2019.808.346 10.1007/s44163-023-00065-5 Olujimi, P.A., Ade-Ibijola, A. (2023) NLP techniques for automating responses to customer queries: a systematic review. Discov Artif Intell 3, 20. https://doi.org/10.1007/s44163-023-00065-5 10.22399/ijcesen.1018 D. Naga Jyothi, & Uma N. Dulhare. (2025). Understanding and Analysing Causal Relations through Modelling using Causal Machine Learning. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1018 10.22399/ijcesen.839 N.B. Mahesh Kumar, T. Chithrakumar, T. Thangarasan, J. Dhanasekar, & P. Logamurthy. (2025). AI-Powered Early Detection and Prevention System for Student Dropout Risk. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.839 10.22399/ijcesen.782 P. Rathika, S. Yamunadevi, P. Ponni, V. Parthipan, & P. Anju. (2024). Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.782 10.22399/ijcesen.1157 Krishna Kumaar Ragothaman. (2025). Smart Distribution in E-Commerce: Harnessing Machine Learning and Deep Learning Approaches for Improved Logistics. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.1157 K. Tamilselvan, , M. N. S., A. Saranya, D. Abdul Jaleel, Er. Tatiraju V. Rajani Kanth, & S.D. Govardhan. (2025). Optimizing data processing in big data systems using hybrid machine learning techniques. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.936 10.22399/ijcesen.560 Naresh Babu KOSURI, & Suneetha MANNE. (2024). Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.560 10.22399/ijcesen.780 J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.780 10.22399/ijasrar.19 Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.19 10.22399/ijasrar.18 Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.18