Data Driven Crop, Fertilizer and Analytics Guidance through Machine Learning

Dhatchinamoorthy, Nandhakumar; Ilham, Vickky; Khaleel, Farra

doi:10.25673/121023

Proceedings of International Conference on Applied Innovation in IT · 2025/07/26 · Vol. 13 · Issue 3 · pp. 287–295

Data Driven Crop, Fertilizer and Analytics Guidance through Machine Learning

Nandhakumar Dhatchinamoorthy, Vickky Anggara Ilham, Farra Sasmita and Reem Abduljaleel Khaleel

📄 Download PDF DOI: 10.25673/121023

Abstract

Farmers still face many difficulties in today's technological world. NLP-powered conversational AI (CAI) chatbots can consistently help farmers across diverse areas of farming, delivering positive economic impacts. Modern technological innovations are being adopted by agricultural firms to significantly reduce operational costs, increase revenues, automate labor-intensive processes, and drive sustainable growth. This study follows a similar approach for agriculture – a sector critically employing approximately 71% of rural Indians. Natural Language Processing (NLP), a core subfield of AI, enables computers to recognize, understand, and analyze human languages effectively and is integral to Conversational AI systems. Economic challenges, climate change, and environmental factors – including poor soil quality, adverse weather patterns, water contamination, and difficult terrain – profoundly affect farming productivity. Despite these persistent hardships, farmers work tirelessly to feed the world's rapidly expanding population. A specialized CAI bot for agriculture was developed to provide timely, on-demand assistance to farmers on critical farming and market-related issues year-round.

Keywords

NLP IoT Farming Conversational AI Knowledgebase Smart Agriculture.

References

P. S. Venkata Reddy, K. S. Nandini Prasad, and C. Puttamadappa, "SASI: Smart Agriculture System Based on IoT," European Journal of Molecular & Clinical Medicine, vol. 7, no. 8, 2020.
J. Vijayalakshmi and K. PandiMeena, "Agriculture TalkBot Using AI," International Journal of Recent Technology and Engineering, vol. 8, no. 2S5, Jul. 2019.
E. Haller and T. Rebedea, "Designing a Chat-bot that Simulates an Historical Figure," in Proc. 19th Int. Conf. on Control Systems and Computer Science, Bucharest, 2013.
P. Srivastava and N. Singh, "Automatized Medical Chatbot (Medibot)," in Proc. 2020 Int. Conf. on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, 2020, pp. 351–354. [Online]. Available: https://doi.org/10.1109/PARC49193.2020.236624.
P. Suebsombut et al., "Chatbot Application to Support Smart Agriculture in Thailand," in Proc. 2022 Joint Int. Conf. on Digital Arts, Media and Technology with ECTI Northern Section Conf. on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), Chiang Rai, Thailand, 2022, pp. 364–367. [Online]. Available: https://doi.org/10.1109/ECTIDAMTNCON53731.2022.9720318.
A. Wahal, M. Aggarwal, and T. Poongodi, "IoT Based Chatbots Using NLP and SVM Algorithms," in Proc. 2022 3rd Int. Conf. on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2022, pp. 484–489. [Online]. Available: https://doi.org/10.1109/ICIEM54221.2022.9853095.
P. A. et al., "Mobile Application for Hospital Management System," in Proc. 2021 5th Int. Conf. on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 1434–1437. [Online]. Available: https://doi.org/10.1109/ICICCS51141.2021.9432286.
R. Sahoo, B. R. Das, and B. K. Mishra, "Analysis and Implementation of Odia Part of Speech Tagger in Recent IoT Based Devices Through Chatbot: A Review," in Proc. 2020 Int. Conf. on Computer Science, Engineering and Applications, 2020.
R. S. Jebur, M. H. B. M. Zabil, L. K. Cheng, and D. A. Hammood, "Image Denoising in Deep Learning: A Comprehensive Survey," EETJ, vol. 1, no. 1, pp. 1–12, Jun. 2024.
R. Parkar et al., "AI and Web-Based Interactive College Enquiry Chatbot," in Proc. 2021 13th Int. Conf. on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, Romania, 2021, pp. 1–5. [Online]. Available: https://doi.org/10.1109/ECAI52376.2021.9515065.
S. Meshram et al., "Conversational AI: Chatbots," in Proc. 2021 Int. Conf. on Intelligent Technologies (CONIT), Hubli, India, 2021, pp. 1–6. [Online]. Available: https://doi.org/10.1109/CONIT51480.2021.9498508.
C. E. Kaed, A. Ponnouradjane, and D. Shah, "A Semantic Based Multi-Platform IoT Integration Approach from Sensors to Chatbots," in Proc. 2018 Global Internet of Things Summit (GIoTS), Bilbao, Spain, 2018, pp. 1–6. [Online]. Available: https://doi.org/10.1109/GIOTS.2018.8534520.
S. K. S. et al., "Chatbot - Attendance and Location Guidance System (ALGs)," in Proc. 2021 3rd Int. Conf. on Signal Processing and Communication (ICPSC), Coimbatore, India, 2021, pp. 718–722. [Online]. Available: https://doi.org/10.1109/ICSPC51351.2021.9451793.
A. T. Mohammad and J. Parchami, "Improving Diabetic Patients Monitoring System Using (NCA-CNN) Algorithm Based on IoT," JT, vol. 6, no. 2, pp. 9–17, Jun. 2024.
P. Anghelescu and S. V. Nicolaescu, "Chatbot Application Using Search Engines and Teaching Methods," in Proc. 2018 10th Int. Conf. on Electronics, Computers and Artificial Intelligence (ECAI), Iasi, Romania, 2018, pp. 1–6. [Online]. Available: https://doi.org/10.1109/ECAI.2018.8678948.
T. Parthornratt et al., "A Smart Home Automation via Facebook Chatbot and Raspberry Pi," in Proc. 2018 2nd Int. Conf. on Engineering Innovation (ICEI), Bangkok, Thailand, 2018, pp. 52–56. [Online]. Available: https://doi.org/10.1109/ICEI18.2018.8448761.
T. Krongthong and B. Muangmeesri, "Modeling and Simulink of Smart Agriculture Using IoT Framework," in Proc. 2019 1st Int. Conf. on Cybernetics and Intelligent System (ICORIS), Denpasar, Indonesia, 2019, pp. 185–188. [Online]. Available: https://doi.org/10.1109/ICORIS.2019.8874914.