AI-Powered Predictive Analytics for Smart Agriculture

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.