AI-Powered Predictive Analytics for Smart Agriculture
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ID: 309123
2022
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Abstract
Smart agriculture increasingly relies on predictive analytics to optimize crop yields, conserve inputs, and manage climate risk. This article synthesizes recent advances in AI—covering feature engineering with multispectral/thermal imagery, Internet of Things (IoT) sensor fusion, spatiotemporal deep learning, and probabilistic forecasting—to build robust decision-support systems for Pakistani and comparable smallholder contexts. We propose an integrated pipeline that ingests satellite (e.g., NDVI/EVI), in-field telemetry (soil moisture, EC, canopy temp), market/meteorological feeds, and management logs. Models such as Gradient Boosting, Temporal Convolutional Networks, LSTM/Transformers, and Gaussian Processes are benchmarked for yield, irrigation, disease, and price predictions. We discuss data governance, explainability, uncertainty quantification, and MLOps for edge-to-cloud deployments. Results from representative case scenarios suggest 10–25% input savings and 8–15% yield improvement when AI recommendations are followed, with payback periods under two seasons for many crops. The paper concludes with a roadmap for inclusive, climate-resilient, and ethically governed agri-AI in South Asia.
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| Authors | Ayesha N. Khan, Muhammad Ali Siddiqui, Sana Raza |
| Journal | International journal of advanced sciences and computing |
| Year | 2022 |
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| Keywords | Keywords not found |
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