Learning from Data to Optimize Control in Precision Farming

TL;DR

This paper discusses how data-driven machine learning and statistical tools are used to optimize control strategies in precision farming, aiming to meet increased food demand sustainably.

Contribution

It introduces recent advances in statistical inference, machine learning, and optimal control techniques applied specifically to precision farming.

Findings

Enhanced predictive models for crop yield

Improved resource management strategies

Integration of IoT data for real-time decision making

Abstract

Precision farming is one way of many to meet a 70 percent increase in global demand for agricultural products on current agricultural land by 2050 at reduced need of fertilizers and efficient use of water resources. The catalyst for the emergence of precision farming has been satellite positioning and navigation followed by Internet-of-Things, generating vast information that can be used to optimize farming processes in real-time. Statistical tools from data mining, predictive modeling, and machine learning analyze pattern in historical data, to make predictions about future events as well as intelligent actions. This special issue presents the latest development in statistical inference, machine learning and optimum control for precision farming.