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2025 Vol.7, Issue 4 Preview Page

Review Article

Non-contact yield measurement during harvesting operations for major root vegetables in Korea: A review
Pabel Kanti Dey1
,
Md Nasim Reza12
,
Ezatullah Zakir1
,
Samsuzzaman1
,
Kyu-Ho Lee12
,
Da-Hye Jeong3
,
Sun-Ok Chung12*
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
3Hyundai Agricultural Machinery Co., Ltd., Iksan 54584, Republic of Korea
*Corresponding Author
31 December 2025. pp. 397-420
PDF XML Citation
Abstract

The increasing demand for food production amid declining labor availability and shrinking agricultural land necessitates the adoption of advanced monitoring and automation technologies in root vegetable production. Root crops such as potato, radish, carrot, and sweet potato are vital to food security and dietary health in Korea; however, yield estimation traditionally relies on manual sampling and post-harvest weighing, leading to inefficiencies and limited spatial insight. This review presented an overview of non-contact yield monitoring techniques applicable to root vegetable crops, highlighting core sensing, data processing methods, and field integration approaches. Optical imaging, particularly RGB and RGB-D systems, currently offers cost-effective solutions for tuber detection, grading, and morphology measurement, though performance remains sensitive to illumination and soil variability. LiDAR-based techniques enable accurate 3D volumetric modeling but face constraints related to cost, vibration disturbances, and computational load. Ultrasonic, thermal, radar, and hyperspectral sensors provide complementary information on crop structure and physiological status but are best suited as auxiliary sources within multimodal frameworks. Data processing advancements, including deep learning based segmentation and sensor fusion, significantly improve real-time accuracy and robustness under dynamic harvesting conditions. In the context of Korea’s evolving mechanized agriculture, integrating these technologies into harvester platforms can provide spatially enabled, real-time yield intelligence to strengthen supply chain logistics and farm-level decision-making. The review concluded with key challenges and future directions to accelerate commercialization and adoption of smart yield monitoring systems for sustainable root crop production.

Keywords
Precision Agriculture
Root crop
Image processing
LiDAR sensing
Machine learning
Yield estimation
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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 : 7
  • No :4
  • Pages :397-420
  • Received Date : 2025-11-23
  • Revised Date : 2025-12-09
  • Accepted Date : 2025-12-09
  • DOI :https://doi.org/10.22765/pastj.20250027
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