In modern agriculture, there is a need for precise and timely analysis of climate data for its sustainability. In smart farming, real-time insight is scheduled for the informed decision taking but the traditional ways of data collection and processing does not support such fast and accurate measurements. This study proposes an edge computing-based system to tackle this challenge to minimize the latency by performing the data analysis on the data source, or on the edge. It proposes the integration of edge cloud computing with real-time climate collection so that the condition of dynamic environment can be continuously analyzed. By doing data processing at the edge, latency is reduced and the accuracy of the predictions is improved at the same time, so the farmers are able to take data driven decision when it matters most. Real time datasets coming from sensors of a smart farming setup were used to test the system. The results shows 35% saved data processing latency and 92% of the prediction accuracy in climate conditions and anomalies. Such outcomes allowed the scheduling of effective irrigation and the decision making on crop management, which demonstrated the potential of edge computing to replace conventional farming with more efficient, data based methods.This implementation is to emphasize the edge computing led transformative opportunities for sustainable and productive agriculture by enabling faster and more actionable decision making.
Shnain et al. (2025) studied this question.