Smart AgriTech: An IoT and Machine Learning-Based Crop Recommendation and Soil Monitoring System with Telugu Chatbot Support

DOI

10.2991/978-94-6239-713-2_28How to use a DOI?

Keywords

Crop recommendation; machine learning; IoT; NPK analysis; smart agriculture; precision farming

Abstract

Agriculture is a key contributor to the economy of the Telangana region and also provides jobs for a large part of its rural population. However, agriculture traditionally relies on farmers’ years of experience and does not incorporate scientific principles into the decision-making process. As a result, agriculture is affected by climate change, soil erosion, and less productive crops due to poor farming practices. This paper discusses the development of a smart agri-tech system that uses the Internet of Things (IoT) and machine learning to support data-driven decisions in agriculture. The system includes data analysis based on nutrient levels, including nitrogen (N), phosphorus (P), and potassium (K) so that farmers can be given recommended N, P, and K for the different crops they plan to plant and how best to allocate their land to each crop. Five different types of machine learning models were tested for this study using various performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 score. The results of the study confirmed that the gradient boosting algorithm performed best in providing the most reliable predictions and in providing the highest accuracy. The Smart AgriTech system is provided through a web-based application that is available to local farmers (in their local language of Telugu) to promote the adoption of sustainable farming practices.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pobbathi Vignesh
AU  - Pinnani Mokshagna
AU  - Sai Sharan Rachamalla
AU  - Kodidala Bharath
AU  - Gongoora Narsamma
PY  - 2026
DA  - 2026/06/25
TI  - Smart AgriTech: An IoT and Machine Learning-Based Crop Recommendation and Soil Monitoring System with Telugu Chatbot Support
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 376
EP  - 382
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_28
DO  - 10.2991/978-94-6239-713-2_28
ID  - Vignesh2026
ER  -