Reinforcement learning (RL), including multi-armed bandits, is a branch of machine learning
that deals with the problem of sequential decision-making in uncertain and unknown …

Digital Twins have gained attention in various industries by creating virtual replicas of real-
world systems through data collection and machine learning. These replicas are used to run …

As greenhouses are being widely adopted worldwide, it is important to improve the energy
efficiency of the control systems while accurately regulating their indoor climate to realize …

The greenhouse system plays a crucial role to ensure an adequate supply of fresh food for
the growing global population. However, maintaining an optimal growing climate within a …

Due to the high efficiency and less weather dependency, autonomous greenhouses provide
an ideal solution to meet the increasing demand for fresh food. However, managers are …

In this study, we present a novel smart greenhouse control algorithm that optimizes crop
yield while minimizing energy consumption costs. To achieve this, we relied on both a …

Due to the complex coupling of greenhouse environments, a number of challenges have
been encountered in the research of automatic control in Venlo greenhouses. Most …

Maximizing profit is usually the objective of optimal control of greenhouse cultivation.
However, due to the problem of “the curse of dimensionality”, the global optimization of …

This work proposes a novel deep reinforcement learning (DRL) based control framework for
greenhouse climate control. This framework utilizes a neural network to approximate state …

The rapidly growing global population presents challenges and demands for efficient
production of healthy fresh food. Autonomous greenhouse equipped with standard sensors …