AI and IoT-Driven Framework for Predictive Urban Agriculture: Bridging Technology and Sustainability
Bibliographic record
Abstract
Urban agriculture offers a sustainable solution to challenges such as food insecurity, environmental degradation, and resource inefficiency in urban settings. This paper presents a hybrid data-driven framework integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies to optimize site selection, resource allocation, and crop yield prediction. The framework leverages Spiking Neural Networks (SNN) for temporal data analysis and Reinforcement Learning (RL) for dynamic decision-making, effectively utilizing publicly available datasets such as the Purdue agricultural sensor dataset. Key contributions include a robust preprocessing pipeline for temporal feature engineering, a scalable decision-making layer for actionable recommendations, and a visualization platform for stakeholder engagement. The results demonstrate the framework’s superior performance, achieving a prediction accuracy of 92% with a Mean Absolute Error (MAE) of 0.11, significantly outperforming state-of-the-art approaches such as Long Short-Term Memory (LSTM) networks and Genetic Algorithms. The RL model achieves a $\mathbf{1 5 \%}$ improvement in resource efficiency, validating its adaptability to real-time environmental changes. This study bridges the gap between AI and IoT integration in urban agriculture, offering a scalable and sustainable framework for transforming urban food systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".