Low Illumination Soybean Plant Reconstruction and Trait Perception

Huang, Yourui; Liu, Yuwen; Xu, Shanyong; Fu, Jiahao

doi:10.3390/agriculture12122067

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…(2017) used this technology to obtain 3D models of strawberries and used custom software to process point cloud data and obtain seven agronomic traits of strawberries. Huang et al. (2022) used the DoidiltenGAN image enhancement algorithm combined with SFM-MVS algorithm to develop a set of agricultural equipment that could accurately perceive the growth of crops under low light.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of agriculture, He et al (2017) used this technology to obtain 3D models of strawberries and used custom software to process point cloud data and obtain seven agronomic traits of strawberries. Huang et al (2022) used the DoidiltenGAN image enhancement algorithm combined with SFM-MVS algorithm to develop a set of agricultural equipment that could accurately perceive the growth of crops under low light. Hui et al (2018) used this technology to obtain 3D point clouds for cucumbers with flat leaves, peppers with small leaves, and eggplants with curly leaves.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN

He¹,

Ye²,

Li³

et al. 2023

Front. Plant Sci.

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Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN

He¹,

Ye²,

Li³

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

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Signage Detection Based on Adaptive SIFT

Wang,

Chen,

Shao

et al. 2023

Smart Innovation, Systems and Technologies

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Physical Structure Expression for Dense Point Clouds of Magnetic Levitation Image Data

Zhang

Shen

et al. 2023

Sensors

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The research and development of an intelligent magnetic levitation transportation system has become an important research branch of the current intelligent transportation system (ITS), which can provide technical support for state-of-the-art fields such as intelligent magnetic levitation digital twin. First, we applied unmanned aerial vehicle oblique photography technology to acquire the magnetic levitation track image data and preprocessed them. Then, we extracted the image features and matched them based on the incremental structure from motion (SFM) algorithm, recovered the camera pose parameters of the image data and the 3D scene structure information of key points, and optimized the bundle adjustment to output 3D magnetic levitation sparse point clouds. Then, we applied multiview stereo (MVS) vision technology to estimate the depth map and normal map information. Finally, we extracted the output of the dense point clouds that can precisely express the physical structure of the magnetic levitation track, such as turnout, turning, linear structures, etc. By comparing the dense point clouds model with the traditional building information model, experiments verified that the magnetic levitation image 3D reconstruction system based on the incremental SFM and MVS algorithm has strong robustness and accuracy and can express a variety of physical structures of magnetic levitation track with high accuracy.

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Low Illumination Soybean Plant Reconstruction and Trait Perception

Cited by 3 publications

References 30 publications

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN

Signage Detection Based on Adaptive SIFT

Physical Structure Expression for Dense Point Clouds of Magnetic Levitation Image Data

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