  In the evolution of agriculture to its next stage, Agriculture 5.0,
artificial intelligence will play a central role. Controlled-environment
agriculture, or CEA, is a special form of urban and suburban agricultural
practice that offers numerous economic, environmental, and social benefits,
including shorter transportation routes to population centers, reduced
environmental impact, and increased productivity. Due to its ability to control
environmental factors, CEA couples well with computer vision (CV) in the
adoption of real-time monitoring of the plant conditions and autonomous
cultivation and harvesting. The objective of this paper is to familiarize CV
researchers with agricultural applications and agricultural practitioners with
the solutions offered by CV. We identify five major CV applications in CEA,
analyze their requirements and motivation, and survey the state of the art as
reflected in 68 technical papers using deep learning methods. In addition, we
discuss five key subareas of computer vision and how they related to these CEA
problems, as well as eleven vision-based CEA datasets. We hope the survey will
help researchers quickly gain a bird-eye view of the striving research area and
will spark inspiration for new research and development.
