A Novel Approach to Optimize Key Limitations of Azure Kinect DK for Efficient and Precise Leaf Area Measurement

Ziang Niu, Ting Huang, Chengjia Xu, Xinyue Sun, Mohamed Farag Taha, Yong He and Zhengjun Qiu ()
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Ziang Niu: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Ting Huang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Chengjia Xu: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Xinyue Sun: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Mohamed Farag Taha: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yong He: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Zhengjun Qiu: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China

Agriculture, 2025, vol. 15, issue 2, 1-20

Abstract: Maize leaf area offers valuable insights into physiological processes, playing a critical role in breeding and guiding agricultural practices. The Azure Kinect DK possesses the real-time capability to capture and analyze the spatial structural features of crops. However, its further application in maize leaf area measurement is constrained by RGB–depth misalignment and limited sensitivity to detailed organ-level features. This study proposed a novel approach to address and optimize the limitations of the Azure Kinect DK through the multimodal coupling of RGB-D data for enhanced organ-level crop phenotyping. To correct RGB–depth misalignment, a unified recalibration method was developed to ensure accurate alignment between RGB and depth data. Furthermore, a semantic information-guided depth inpainting method was proposed, designed to repair void and flying pixels commonly observed in Azure Kinect DK outputs. The semantic information was extracted using a joint YOLOv11-SAM2 model, which utilizes supervised object recognition prompts and advanced visual large models to achieve precise RGB image semantic parsing with minimal manual input. An efficient pixel filter-based depth inpainting algorithm was then designed to inpaint void and flying pixels and restore consistent, high-confidence depth values within semantic regions. A validation of this approach through leaf area measurements in practical maize field applications—challenged by a limited workspace, constrained viewpoints, and environmental variability—demonstrated near-laboratory precision, achieving an MAPE of 6.549%, RMSE of 4.114 cm 2 , MAE of 2.980 cm 2 , and R 2 of 0.976 across 60 maize leaf samples. By focusing processing efforts on the image level rather than directly on 3D point clouds, this approach markedly enhanced both efficiency and accuracy with the sufficient utilization of the Azure Kinect DK, making it a promising solution for high-throughput 3D crop phenotyping.

Keywords: 3D; phenotyping; misalignment; depth quality; inpainting (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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