Spectral monitoring of wheat leaf nitrogen content based on canopy structure information compensation

Li, Huaimin; JingChao, Zhang; Xu, Ke; Jiang, Xiaoping; Zhu, Yan; Cao, Weixing; Ni, Jun

doi:10.1016/j.compag.2021.106434

Cited by 8 publications

(7 citation statements)

References 43 publications

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“…Plant structure reflects the size and organization form of above-ground organs of crops [ 11 ], which can indicate growth status, cultivation conditions, and water and fertilizer measures of crops [ 12 ]. In addition, the phenotypic traits of plants such as height and width also provide references for breeders to cultivate excellent breeding [ 13 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant

et al. 2023

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Background Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. Fast and accurate are always the goals of the plant 3D model reconstruction research. Results We proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits’ height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was also carried out during the experiment processing. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result showed that the average accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. Conclusions The reconstruction method for the 3D model of the peanut plant described in this paper is capable of rapidly and accurately establishing a 3D model of the peanut plant while also meeting the modeling requirements for other species' breeding processes. This study offers a potential tool to further explore the 3D model for improving traits and agronomic qualities of plants.

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Section: Introductionmentioning

confidence: 99%

Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant

et al. 2023

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“…However, it is difficult to universally apply the disadvantages of the above methods, such as destructiveness, high cost of analysis, and time-consuming and labor-intensive analysis. Studies have shown that spectral reflectance is closely related to leaf nitrogen content, which is an important indicator to reflect its yield and quality [3]. Therefore, it is of great significance to build a spectral inversion model for nitrogen content in winter wheat canopy leaves with high universality and high accuracy to improve monitoring efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this study we propose fusion multimodal features of the canopy spectrum, aiming to improve the estimation accuracy of the leaf nitrogen content in wheat. The objectives of this study were (1) to design a convolutional neural network to obtain deep semantic information and better express the features of the canopy spectrum, (2) to fuse multimodal features including spectral features (VIs + BPs) and convolutional features of the wheat canopy spectrum, and (3) to build an estimation model based on fusion multimodal features for wheat LNC, aiming to obtain good performance.…”

Section: Introductionmentioning

confidence: 99%

Estimating Leaf Nitrogen Content in Wheat Using Multimodal Features Extracted from Canopy Spectra

et al. 2022

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The leaf nitrogen content (LNC) of wheat is one of key bases for wheat nitrogen fertilizer management and nutritional diagnosis, which is of great significance to the sustainable development of precision agriculture. The canopy spectrum provides an effective way to monitor the nitrogen content of wheat. Previous studies have shown that features extracted from the canopy spectrum, such as vegetation indices (VIs) and band positions (BPs), have successfully achieved the monitoring of crop nitrogen nutrition. However, the features mentioned above are spectral features extracted on the basis of linear or nonlinear combination models with a simple structure, which limits the general applicability of the model. In addition, models based on spectral features are prone to overfitting, which also reduces the accuracy of the model. Therefore, we propose an estimation model based on multimodal features (convolutional features and VIs, BPs) of the canopy spectrum, which aim to improve accuracy in estimating wheat LNC. Among these, the convolutional features (CFs) extracted by the designed convolutional neural network represent the deep semantic information of the canopy reflection spectrum, which can make up for the lack of robustness of the spectral features. The results showed that the accuracy of the model based on the fusion features (VIs + BPs + CFs) was higher than that of the feature of single modality. Moreover, the particle swarm optimization–support vector regression (PSO-SVR) model based on multimodal features had the best prediction effect (R2 = 0.896, RMSE = 0.188 for calibration, R2 = 0.793, RMSE = 0.408 for validation). Therefore, the method proposed in this study could improve performance in the estimation of wheat LNC, which provides technical support for wheat nitrogen nutrition monitoring.

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“…Plant structure reflects the size and organization form of above-ground organs of crops [11], which can be used to indicate growth status, cultivation conditions, and water and fertilizer measures of crops [12]. The morphological structure of the crop canopy can affect photosynthetic efficiency through its ability to intercept light [13,14], and ultimately influence yield [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…The morphological structure of the crop canopy can affect photosynthetic efficiency through its ability to intercept light [13,14], and ultimately influence yield [15,16]. The most important evaluation indices of the canopy are the number and geometric distribution of leaves [11]. Small inclination angles of leaves increase the photosynthetic efficiency of the whole canopy [17], and erect leaves shed more radioactive heat, which improves the heat tolerance of crops [18] and creates a "micro-climate" with the canopy [19].…”

Section: Introductionmentioning

confidence: 99%

Fast Reconstruction Method of Three-dimension Model Based on Dual RGB-D Cameras for Peanut Plant

Liu

Yuan

Zhao

et al. 2022

Preprint

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Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. A 3D reconstruction method based on dual RGB-D cameras was proposed for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits’ height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was carried out at the same time. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result shows that the synthetic accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. This approach should be useful for 3D modeling and phenotyping peanut breeding.

show abstract

Spectral monitoring of wheat leaf nitrogen content based on canopy structure information compensation

Cited by 8 publications

References 43 publications

Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant

Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant

Estimating Leaf Nitrogen Content in Wheat Using Multimodal Features Extracted from Canopy Spectra

Fast Reconstruction Method of Three-dimension Model Based on Dual RGB-D Cameras for Peanut Plant

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