Deep Learning and Reinforcement Learning Methods for Advancing Sustainable Agricultural and Natural Resource Management

Sustainable agriculture and natural resource management solutions depend on and vary with the requirements and objectives of the stakeholders, typically encompassing varying spatial scales and input data sources. Therefore, futuristic natural resource management techniques involve optimized resource allocation and extend to the trans-disciplinary applications entailing multimodal data assimilation and multi-objective decision-making. Since agricultural practices are tightly coupled with the availability and exploitation of natural resources, this chapter discusses how deep learning and reinforcement learning techniques improve agricultural automation while minimizing their inimical environmental effects. The chapter contains incrementally (conceptually) structured sections to help establish a learning trajectory for the readers. It begins with the latest developments in deep learning algorithms and frameworks for precision agriculture and multiscale agricultural sensing. The subsequent sections discuss the deep reinforcement learning frameworks built as wrappers over stand-alone deep-learning or computer vision modules to enable the actuation of cyber-agricultural systems for sustainable natural resource management: for example, how Intelligent Agent Models are developed by integrating geospatial agent-based modeling with reinforcement learning to provide multi-objective decision-making solutions. The future directions of sustainable agricultural development are also provided by reviewing simulation-based studies on the sensitivity analyses of the influential and confounding factors in decision-making.