AI-Enabled Smart Agriculture: A Sustainable Approach to Rural Development Using Structural Equation Modelling
Bibliographic record
Abstract
To better understand how AI-enabled smart agriculture affects sustainable rural development, this study examines the effects of five major independent variables (IVs) that together make up the construct of AI-Enabled Smart Agriculture: Farmers' Knowledge and Acceptance, AI Technology Adoption, Precision Farming Techniques, Policy and Infrastructure Support, and Resource Efficiency.Farm productivity is the result of this construct, and it has an impact on the results of rural development.These correlations were investigated using an SEM technique.Data was gathered from 525 respondents who represented five major stakeholder groups: community representatives (NGOs/Cooperatives), policy makers and local government officials, agricultural experts and extension officers, technology providers (AgriTech Companies), and farmers (primary respondents).CFA was the first method used in the study to confirm the measurement model.With all AVE values above the 0.50 cutoff and CR values over 0.70, the CFA results validated convergent validity and showed that the constructs were accurately measured.The Fornell-Larcker Criterion was also used to establish discriminant validity, which confirmed that the constructs were unique when the square root of AVE for each construct was higher than its correlations with other constructs.The measurement model is fit, according to these findings.The proposed relationships were then tested using SEM.With the following indices: CFI = 0.962, TLI = 0.946, NFI = 0.955, RMSEA = 0.096, and RMR = 0.014, the SEM model, which partially mediates the association between AI adoption and rural development, demonstrated excellent model fit.AI-Enabled Smart Agriculture (made up of the five IVs) leads to Farm Productivity.According to the SEM results, Farm Productivity was highly impacted by the IVs in the AI-Enabled Smart Agriculture construct.A partial mediating function was then played by farm productivity, which improved the results of rural development, including infrastructural development, social well-being, economic growth, and environmental sustainability.These results demonstrate how important AIenabled smart agriculture is for raising farm productivity, which in turn serves as a partial mediator for rural development.The study emphasizes how crucial it is to integrate technology, enhance farmers' understanding and acceptance, and offer strong policy and infrastructure support to support sustainable farming methods and long-term rural development.For policymakers, tech developers, and agricultural stakeholders looking to use AI for sustainable rural development, this study provides important new insights.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".