Enhancing Agricultural Productivity through Artificial Intelligence
DOI:
https://doi.org/10.5281/zenodo.19694459Keywords:
Artificial Intelligence, Agriculture, Productivity, SustainabilityAbstract
Purpose: To explore the potential of Artificial Intelligence (AI) to revolutionize the agricultural industry in Pakistan to overcome major obstacles, which include poor productivity, climate disruptions, poor resource management, and after-harvest losses.
Methodology: The paper takes the form of literature review of the current AI usage in agriculture and a qualitative analysis of the case studies in international, regional and national settings. It is based on secondary data sources such as peer review articles, reports, and documented field implementations. It has a tiered estimation model (Conservative, Moderate, Optimistic) that forecasts possible results based on the viewpoints of the policymakers, donors, and implementers.
Findings: The research concludes that AI-driven drones, IoT-based monitoring systems, and advisory apps can be highly efficient in enhancing the efficiency and productivity of agriculture. The feasibility and effectiveness of adoption of AI are supported by evidence based on pilot projects, such as the Pakistan-China Joint Lab and the Land Information and Management System. The findings show a huge possibility to improve food security, the income of farmers, and climate resilience.
Implications/Originality/Value: The paper identifies the necessity of a national policy on AI in agriculture, enhancing digital connectivity, and specific subsidies that will promote it. It has its contribution by integrating the analysis of policy with the evidence of practical cases and a tiered projection model, providing a systematic perspective on the AI-based agricultural transformation in Pakistan.
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