Intelligent Farm Based on Deep Reinforcement Learning for optimal control

Smart agriculture is the use of automation, sensors, and data to improve crop yields, reduce costs and increase crop quality. Predictive analytics, machine learning, and artificial intelligence are used. Another benefit of using artificial intelligence in agriculture is the ability to optimize the consumption of resources such as water and fertilizer to achieve the greatest yield. However, to date, the technology has not been used to optimize the management of agricultural operations. The objective of this paper is to introduce the concept of smart farming and discuss applying deep reinforcement learning for optimal management in the field. We propose a smart farming system that uses Deep Reinforcement Learning to optimize the operation of a farm. The system uses Deep Learning to recognize patterns in a large dataset of real-world agricultural data, such as crop yields and weather, and uses this knowledge to recommend actions. It also dynamically learns optimal control policies for a variety of crops, depending on the farmer's goals and current conditions.