Urban AgriFlo: AI-Driven Demand Forecasting and Geospatial Optimization for Sustainable Urban Food Supply Chains

DOI

10.2991/978-94-6463-866-0_21How to use a DOI?

Keywords

AI; Urban Farming; Demand Forecasting; Geolocation; ML; NLP; Chatbot; FAISS

Abstract

Most of the crop supply systems in urban areas face multiple challenges such as food waste, limited access for small scale producers and excessive costs. This model covers all agri-producers- both farmers and home growers in urban context. This paper presents Urban AgriFlo an all-in-one AI powered platform that optimizes crop production and distribution in urban areas, with the help of demand forecasting, geolocation services and SMS alerts. With the help of machine learning models trained on sample data, Urban AgriFlo can give out close to accurate forecasting. Farmers can actively list their produce on the platform, enabling direct engagement with the consumers. Additionally, an NLP based chatbot for both producers and consumers helping them in real time by getting relative market insights via FAISS based vector searches that give them details about the benefits of fresh crops. By accurately predicting the demand, Urban AgriFlo can reduce food miles drastically thus keeping the crops fresh by the time they arrive to the consumer.

Copyright

© 2025 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  - Aman Chinmai Dev Bondla
AU  - Shreya Tigga
AU  - M. S. Murali Dhar
PY  - 2025
DA  - 2025/10/31
TI  - Urban AgriFlo: AI-Driven Demand Forecasting and Geospatial Optimization for Sustainable Urban Food Supply Chains
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 234
EP  - 246
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_21
DO  - 10.2991/978-94-6463-866-0_21
ID  - Bondla2025
ER  -