Thulir: AI-Powered Smart Agriculture Platform for Crop Recommendation, Climate Alerts, and Resource Optimization

Santhosh, M and Bhavna, M and Packialatha, A (2025) Thulir: AI-Powered Smart Agriculture Platform for Crop Recommendation, Climate Alerts, and Resource Optimization. 2nd International Conference on Global Trends in Engineering and Technological Advancement (2nd ICGTETA’25), 2 . GOJAN School of Business and Technology, CHENNAI. ISBN 978-81-993196-8-4

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Abstract

The Thulir Web Application is an AI-powered AgriTech platform designed to help farmers
adopt technology for sustainable farming. It integrates Artificial Intelligence (AI), Machine
Learning (ML), and real-time data analytics to create a digital ecosystem that enhances
decision-making and farm productivity. The platform addresses key agricultural challenges
such as limited access to modern tools, lack of direct market connections, unpredictable
weather, and poor crop planning. Thulir provides multiple smart modules including Tool
Renting, Smart Connect Marketplace, AI-Based Disease Detection, Crop and Yield Prediction,
as well as features like an AI-Based Crop Recommendation System, Climate Alert System, and
Land Area Calculator. Using algorithms such as CNN, Random Forest, KNN, and SVM, the
system recommends crops based on soil pH, humidity, temperature, and rainfall, along with
guidance on fertilizer use and expected yield. The Climate Alert System leverages APIs and AI
to notify farmers of severe weather events such as heavy rain, heatwaves, and storms. The Land
Area Calculator uses GPS and Map APIs for accurate field measurements and better resource
planning. With features including multilingual support, real-time weather forecasts, and
agricultural news updates, Thulir bridges the gap between technology and rural farmers. By
promoting digital inclusion, sustainability, and climate-smart farming, the platform aims to
transform India’s agricultural landscape and enhance the economic resilience of small and
marginal farmers.

Item Type: Book
Subjects: Computer Science Engineering > Cloud Computing
Domains: Computer Science Engineering
Depositing User: IR Admin
Date Deposited: 07 Sep 2026 15:57
Last Modified: 08 Sep 2026 06:45
URI: https://ir.vistas.ac.in/id/eprint/22824

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