A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture

TL;DR

This survey reviews how computer vision technologies are applied in controlled-environment agriculture, highlighting five key applications, datasets, and recent deep learning advancements to foster further research and innovation.

Contribution

It provides a comprehensive overview of CV applications in CEA, analyzing requirements, motivations, and recent research, bridging the gap between AI researchers and agricultural practitioners.

Findings

Identified five major CV applications in CEA.

Surveyed 68 recent deep learning-based studies.

Compiled eleven datasets for vision-based CEA research.

Abstract

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.