Leveraging Artificial Intelligence and Data Analytics for Sustainable, Resilient, and Efficient Food Production

DOI

10.2991/978-2-38476-583-6_11How to use a DOI?

Keywords

Artificial intelligence; Sustainable agriculture; Data analytics; System resilience; Precision food production

Abstract

Climate variability, resource limitation and increase in demand are putting pressure on food production systems, and the limits of traditional and narrowly optimized AI-driven solutions in agriculture. The paper fills this gap by suggesting a hybrid artificial intelligence and data analytics system to conceptualize predictive performance, resource efficiency, and system resilience as a single decision-making system. The framework integrates the multimodal data fusion, predictive and diagnostic learning, and multi-objective optimization, and explicitly incorporates the variables of sustainability and resilience within the analytical heart as opposed to assessing them some posteriori. Experimental testing demonstrates that the suggested strategy brings down the yield forecasting mistake to 8.6% RMSE, versus 10.9%-12.8% found on realistic baseline frameworks. Resource efficiency is enhanced by up to 19.3 percent reduction in water and 16.7% reduction in energy with no capacity to reduce yield stability. The framework under the conditions of artificial environmental noise reaches a resilience index of 0.84% and reduces recovery time to seven days, which beats the current AI-based agricultural models. These findings suggest that the intelligence as an adaptive system level organization can be used to help with the food production strategies, which are efficient, yet robust and sustainable, and contains practical value to the future climate-resilient agricultural decision support systems.

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  - Indra Kishor
AU  - Udit Mamodiya
PY  - 2026
DA  - 2026/06/30
TI  - Leveraging Artificial Intelligence and Data Analytics for Sustainable, Resilient, and Efficient Food Production
BT  - Proceedings of the International Conference on Emerging Food Studies: Intersections of Culture, Science and Sustainability (ICEFS 2026)
PB  - Atlantis Press
SP  - 95
EP  - 113
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-583-6_11
DO  - 10.2991/978-2-38476-583-6_11
ID  - Kishor2026
ER  -