Banana Pseudostem Width Detection Based on Kinect V2 Depth Sensor

Wang, Jinzhi; Li, Xiuhua; Zhou, Yonghua; Wang, Huaihai; Li, Minzan

doi:10.1155/2022/3083647

Cited by 2 publications

(2 citation statements)

References 13 publications

(14 reference statements)

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“…Number 1 and 6 contributed byWang et al (2022), numbers 2 contributed bySong (2019), numbers 3, 4, 7, and 8 contributed by, and numbers 5 and 9 contributed by this study.…”

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confidence: 90%

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

Yang,

Jiang,

Duan

et al. 2024

Front. Plant Sci.

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Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant’s growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers.

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“…Number 1 and 6 contributed byWang et al (2022), numbers 2 contributed bySong (2019), numbers 3, 4, 7, and 8 contributed by, and numbers 5 and 9 contributed by this study.…”

mentioning

confidence: 90%

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

Yang,

Jiang,

Duan

et al. 2024

Front. Plant Sci.

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“…(1) Point cloud processing method Three-dimensional (3D) point clouds facilitate the non-invasive measurement of leaf phenotypic parameters, offering advantages over manual and two-dimensional (2D) methods [7][8][9][10][11]. However, original 3D plant point clouds often include irrelevant ground and noise points, complicating accurate parameter measurements [12].…”

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confidence: 99%

Crop Leaf Phenotypic Parameter Measurement Based on the RKM-D Point Cloud Method

Mu,

Li,

Deng

et al. 2024

Sensors

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Crop leaf length, perimeter, and area serve as vital phenotypic indicators of crop growth status, the measurement of which is important for crop monitoring and yield estimation. However, processing a leaf point cloud is often challenging due to cluttered, fluctuating, and uncertain points, which culminate in inaccurate measurements of leaf phenotypic parameters. To tackle this issue, the RKM-D point cloud method for measuring leaf phenotypic parameters is proposed, which is based on the fusion of improved Random Sample Consensus with a ground point removal (R) algorithm, the K-means clustering (K) algorithm, the Moving Least Squares (M) method, and the Euclidean distance (D) algorithm. Pepper leaves were obtained from three growth periods on the 14th, 28th, and 42nd days as experimental subjects, and a stereo camera was employed to capture point clouds. The experimental results reveal that the RKM-D point cloud method delivers high precision in measuring leaf phenotypic parameters. (i) For leaf length, the coefficient of determination (R2) surpasses 0.81, the mean absolute error (MAE) is less than 3.50 mm, the mean relative error (MRE) is less than 5.93%, and the root mean square error (RMSE) is less than 3.73 mm. (ii) For leaf perimeter, the R2 surpasses 0.82, the MAE is less than 7.30 mm, the MRE is less than 4.50%, and the RMSE is less than 8.37 mm. (iii) For leaf area, the R2 surpasses 0.97, the MAE is less than 64.66 mm2, the MRE is less than 4.96%, and the RMSE is less than 73.06 mm2. The results show that the proposed RKM-D point cloud method offers a robust solution for the precise measurement of crop leaf phenotypic parameters.

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Banana Pseudostem Width Detection Based on Kinect V2 Depth Sensor

Cited by 2 publications

References 13 publications

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

Crop Leaf Phenotypic Parameter Measurement Based on the RKM-D Point Cloud Method

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