SAIL: Sustainable Agricultural Intelligence and Learning — A Unified Framework for LLM-Driven Analytics, Protocol Benchmarking, and Bayesian Deployment Optimization in Agricultural IoT

Abstract

The sustainable deployment of Internet of Things (IoT) networks in precision agriculture requires the simultaneous resolution of three long-standing challenges: energy-constrained operation at scale, robust protocol and framework selection, and accessible, intelligent decision support for regionally diverse farming communities. This paper consolidates and extends three complementary lines of research into a single, unified framework — Sustainable Agricultural Intelligence and Learning (SAIL) — that integrates (i) Large Language Model (LLM)-driven multimodal data analytics with bilingual (English–Punjabi) decision support, (ii) a rigorous multi-protocol, multi-framework performance benchmarking methodology, and (iii) a simulation-driven, field-validated Multi-Objective Bayesian Optimization (MOBO) deployment strategy.

We formulate the joint problem as a stochastic, multi-objective optimization over sensing, communication, computation, and linguistic-accessibility parameters, and support it with information-theoretic, statistical-learning, and Gaussian-process-based theoretical analysis. The unified framework was evaluated across six agricultural testbeds in Punjab, Haryana, and Rajasthan spanning over 1,350 hectares and more than 4,500 IoT nodes.

Results show up to 92.3% energy reduction and 94.7% crop-yield prediction accuracy for the LLM-analytics component; a composite performance score of 0.945 (rank 1 of 96 protocol–framework combinations) for the benchmarked FSEA+LoRaWAN configuration; and, for the simulation-to-field deployment, a 32.7% energy-efficiency improvement, 41.3% latency reduction, and 28.9% coverage enhancement, with simulation-to-reality relative errors below 3.1% across all metrics. Bilingual implementation achieved 90.9% technical-term translation accuracy and 92.1% farmer comprehension.

Statistical testing (ANOVA, Tukey HSD, Wilcoxon signed-rank, Student's t-test) confirms that all reported improvements are highly significant (p < 0.001) with large effect sizes. Collectively, these results demonstrate that combining intelligent analytics, principled optimization, and standardized benchmarking yields agricultural IoT systems that are simultaneously energy-sustainable, technically superior, and regionally accessible, offering a practical and theoretically grounded roadmap for next-generation precision agriculture.