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2024 Vol.6, Issue 2 Preview Page

Review Article

A review on stereo vision for feature characterization of upland crops and orchard fruit trees
Md Rejaul Karim1
,
Shahriar Ahmed1
,
Md Nasim Reza12
,
Kyu-Ho Lee1
,
HongBin Jin2
,
Mohammod Ali1
,
Sun-Ok Chung12*†
,
Joonjea Sung3†
1Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
2Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
3FYD Company Ltd., Suwon 16676, Republic of Korea
*Corresponding Author
†These authors equally contributed to this study as corresponding authors.
30 June 2024. pp. 104-122
PDF XML Citation
Abstract

Characterization of plant features is essential for effective management of plant growth monitoring, and precise management practices in crop production. Stereo vision captures multiple perspective images to create a three-dimensional (3D) representation, enabling thorough analysis of crop structure and morphology. The objective of this study was to review the application of stereo vision in feature characterization of plants and fruit trees. Various features of plants such as height, canopy volume, plant spacing, intra-row spacing, and leaf area were surveyed for their characterization potential along with several data acquisition and data processing algorithms consisting of image segmentation, 3D image reconstruction, depth mapping, and disparity mapping. The study found out some results regarding the feature characterization of plants and fruit trees using stereovision. The tree canopy estimation results showed 6-7% error for elliptical and 2-3% error for conical shaped trees as well as for corn plants detection with an accuracy of 96.7% under natural light conditions. From a maximum distance of 5 cm and 1 cm, the errors were observed with the detection accuracy of 74.6% and 62.3%, respectively. The plant height of cabbage, potato, sesame, radish, and soybean were estimated with a R2 value of 0.78 to 0.84 and with an error less than 5%. Stereo vision achieved 97% precision with an RMSE of 0.016 m in wheat height measurement and distance of 20 m with errors below 5% for hazelnut trees. By addressing challenges and exploring various techniques, the paper concluded by summarizing key findings and suggesting directions for further research in plant growth and crop production practices.

Keywords
Precision agriculture
Stereo vision
Stereo matching
Depth mapping
Orchard
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Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 6
  • No :2
  • Pages :104-122
  • Received Date : 2024-05-10
  • Revised Date : 2024-06-23
  • Accepted Date : 2024-06-24
  • DOI :https://doi.org/10.22765/pastj.20240008
Journal Informaiton Precision Agriculture Science and Technology Precision Agriculture Science and Technology
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