An AI-Driven Framework for Precision Agriculture and Sustainable Crop Optimization

Swaminathan Sankaran

Research Article | Open Access | Download Full Text

Volume 1 | Issue 2 | Year 2025 | Article Id: RLS-V1I2P104

An AI-Driven Framework for Precision Agriculture and Sustainable Crop Optimization

Swaminathan Sankaran

Received	Revised	Accepted	Published
24 Jul 2025	27 Aug 2025	11 Sep 2025	30 Sep 2025

Citation

Swaminathan Sankaran. “An AI-Driven Framework for Precision Agriculture and Sustainable Crop Optimization.” DS Reviews of Research in Life Sciences, vol. 1, no. 2, pp. 33-42, 2025.

Abstract

This paper describes an end-to-end AI framework integrating field sensing, edge computing, and cloud-scale learning with the delivery of site-specific, sustainable crop decisions. Soil moisture and nutrient probes, microclimate stations, machine telemetry, and multimodal imagery from drones and satellites ingest heterogeneous data through an interoperable IoT pipeline and harmonize it in a geospatial feature store. Estimates of the treatment effect of irrigation and fertilizer under real-world confounding come from tree-based learners and spatiotemporal deep networks combined with causal inference. Uncertainty-aware prediction by means of ensembles and calibration feeds a multi-objective optimizer balancing profit, yield stability, water and nitrogen footprints, and carbon outcomes while respecting operational constraints such as pump capacity, labor windows, and regulatory limits. A farmer-in-the-loop decision layer offers explainability to prescriptions and counterfactuals together with safety rails, closing the loop via actuation and continuous monitoring. To improve robustness and equity, the framework advocates for federated training for privacy-preserving collaboration across farms and embeds lifecycle accounting for sensing and computing. Field pilots demonstrate 10–25% yield improvement with 20–50% reductions in water and double-digit reductions in fertilizer and energy use, with accelerated decision cycles from days to hours. There lies the practical road from raw agri-data to climate-resilient agronomy, scaling from smallholder to enterprise operations and aligning productivity with environmental stewardship.

Keywords

Precision agriculture, Causal inference, Graph neural networks, Reinforcement learning, Multi-objective optimization, Uncertainty quantification, Federated learning.

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