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@article{187478,
author = {RAMESHKUMAR S and Boobesh D and Dhaniya M and Keerthana R and Maheshwari S},
title = {Design and Development of an AI-Enhanced Web Application for Market Insights, Weather and Crop Advisory},
journal = {International Journal of Innovative Research in Technology},
year = {2025},
volume = {12},
number = {6},
pages = {4590-4595},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=187478},
abstract = {Small-scale farmers in India face significant challenges, including pest infestations, soil degradation, unpredictable weather, and volatile market prices, which undermine crop yields, income, and food security. Existing applications, such as Uzhavan, Plantix, and Farmrise, often deliver inaccurate, non-localized weather forecasts, resulting in substantial crop losses. This paper introduces a web-based, AI-powered chatbot designed to address these challenges through a mission-driven approach to reduce rural poverty and promote sustainable agriculture. The chatbot integrates pest detection using OpenAI’s Vision API, soil health analysis via machine learning models, hyper-localized weather forecasting with free India-specific APIs, and market price predictions using open data sources. It supports voice interaction in languages including Tamil, English, Telugu, Malayalam, and Hindi to ensure accessibility. Built on Indian datasets and free APIs, with plans to incorporate additional APIs for enhanced accuracy, the system aims to increase yields by up to 30%, reduce losses by 20-25%, and improve market linkages. This scalable, cost-effective solution is tailored for India’s diverse agricultural landscape, with potential for national and global expansion, advancing food security, economic stability, and sustainable farming practices.},
keywords = {Agricultural chatbot, AI assistance, pest detection, soil health monitoring, hyper-local weather forecasting, market price prediction, data-driven agriculture, small-scale farming, multilingual voice support, India, sustainable agriculture.},
month = {November},
}
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