mlsanityData to 14 Oct 2026

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

arXiv 2210.11318 ↗First posted 20 Oct 2022cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 168 of its 388 references that are in the map. First posted on arXiv 20 Oct 2022.

Builds on

168 of 388 references.

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50 most cited of 168 All 168 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2025Internet of Things-Based Smart Precision Farming in Soilless Agriculture: Opportunities and Challenges for Global Food Securitycited by 2
  2. 2026SAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Seriescited by 1
  3. 2024Review of Fruit Tree Image Segmentationcited by 0
  4. 2025AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestockcited by 0
  5. 2026Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Modelscited by 0
  6. 2026Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Predictioncited by 0