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Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gold (1001-1277) is the property of Gold Magazine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate and high-throughput maize plant phenotyping is vital for crop breeding and cultivation research. Ear related phenotypic parameters are important agronomic traits. However, fully automatic and fine ear organ segmentation of maize shoots from three-dimensional (3D) point clouds is still challenging. To address this issue, a skeleton-based maize plant organ segmentation process was used to segment the ear organs of the plant and extract phenotypic parameters. Firstly, the Laplace based skeleton extraction algorithm was utilized to generate plant skeleton. In this study, breadth first search method was used to obtain all the connected branches of the plant skeleton. The connected branches with the largest number of vertices were retained as the plant skeleton, while the small skeletons formed by the other connected branches were deleted to ensure that the plant skeleton is a connected undirected graph. Secondly, the plant skeleton was decomposed into several organ sub skeletons using Dijkstra algorithm, and then the organ sub skeletons were divided into stem sub skeletons and non-stem sub skeletons according to the angle features of sub skeletons and point cloud cylinder features. After that, the organ sub skeletons were used to obtain the seed points of each organ, and then the unsegmented points were classified in the order from the top to the bottom of the plant in turn, to get the final organ segmentation results. Four constraints (organ height constraint, sub-skeleton length constraint, cylindrical feature constraint, and the point cloud number constraint) were used to identify ear organs from all organ instances. Four constraints (organ height constraint, sub-skeleton length constraint, cylindrical feature constraint, and the point cloud number constraint) were used to identify ear organs from all organ instances. Four phenotypic traits, ear height, ear length, ear diameter and the ratio of plant height to ear height, were extracted using the ear organ instance. The segmentation method was tested on 15 maize plants. This study took about 24 seconds to process the maize plant with 10 000 point clouds. The result showed that the proposed method had a strong ability of ear recognition. The ear recognition accuracy was 91.3%. The average F1 score, average precision, and average recall of the all the ear organs were 0.73, 0.82, and 0.70 respectively. Furthermore, to compare with the phenotypic parameters obtained by the proposed method in this paper and those obtained by manual measurement, the regression analysis was done and the results showed that the determination coefficients of ear height, ear length, ear diameter and the ratio of plant height to ear height, were 0.97, 0.78, 0.85, and 0.96, respectively, the root mean square error were 3.23, 4.98, 0.73 cm, and 0.07, respectively. There were also some problems in this method. First of all, if the distance between the ear tip point cloud and the other organ point cloud was too close, the ear skeleton might fail to be extracted, resulting in the ear could not be segmented, which often occurred in the second ear with a smaller volume. Secondly, the ability of the segmentation method to identify the boundary between the ear and other organs needs to be improved, which would lead to false segmentation of the ear point cloud, and more probability of under segmentation. The proposed algorithm cloud extract the point cloud and phenotypic parameters of ear organs. As far as we know, this was the first method to obtain this effect. The proposed approach might play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, and dynamic growth monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a plant organ appearance textures generation system using a single image to improve the efficiency of three-dimensional digital media content production and simplify the tedious process of producing the textured appearance of plant organs in images. The system consists of six modules: diffuse reflection texture, transmittance texture, specular textures, normal map, ambient occlusion (AO) map and real-time visualization. The system takes a single organ texture as input. Firstly, the input texture is decomposed into diffuse reflection texture and shading texture by the diffuse reflection texture module. The two textures thus obtained are also used as input images for other modules. Diffuse reflection texture module adopts an energy constrained intrinsic decomposition method, which is stable and quick. The transmittance texture module can transform a diffuse reflection texture into a transmittance texture. The core of the module is a transmittance empirical model based on the PROSPECT model, which is a widely used leaf-scale radiation transfer model. To obtain this empirical model, we first generate the spectral data of leaf reflectance and transmittance under different parameters by PROSPECT model and transform them into RGB color space. Then we invert the statistical formula of plant leaf transmittance and reflection by using weighted least squares regression. The formula yields the transmittance color by diffuse reflection color. Specular intensity and roughness parameter describe the directional characteristics of leaf surface reflection. In theory, these two parameters can’t be accurately calculated from a single image. Therefore, specular textures module focuses on providing users with an interactive tool to speed up the generation of textures. We assume that the materials of leaf-blade regions that show similar diffuse reflectance are similar, as are their specular parameters. Therefore, in the specular textures module, the user interactively selects some sample points on the diffuse reflection texture, sets the specular intensity parameter and roughness parameter for these points, and then calculates these two specular parameters on the basis of the diffuse similarity between the other positions on the diffuse reflection texture and the selected sample points. Normal map module uses shading map to estimate a normal map. First, the shape from shading technique based on linear approximation is used to estimate the relative height of the organ surface from the shading map. Second, the organ surface height is filtered by Gauss blur to obtain the low-frequency features on the organ surface height. We estimate two normal maps by low frequency height feature and original height feature respectively. We overlay the two normal maps by linear interpolation to obtain the final normal map We observed that the concave and convex areas on the organ surface formed shadows in the shading map and these could be used as environmental shadows. Therefore, the system uses an illumination map S to generate a simple AO map. First, the brightness of the shading map is enhanced, and then, Gauss blur is used to get the final AO map. To facilitate users to evaluate the quality of the final product, that is the appearance of the texture, the real-time visualization module loads the generated appearance of the texture for real-time rendering to provide the users the WYSIWYG three-dimensional visualization results. Experiments show that our appearance of texture matches the appearance parameters in the three-dimensional rendering formula and can be directly applied to the generation of realistic digital images of plants in three-dimensional visualization software. Compared to the current commercial texture production software, the texture style generated by this system is more comprehensive, more in line with the production needs of digital images of plants, and more automated and user-friendly. It meets the need for rapid and accurate rendering of the textured appearance of plant organs and provides a technical tool for the development of three-dimensional digital image resources of agricultural specimens. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we presents a method for modeling and visualization of crop leaves based on agronomic parameters in order to improve the production efficiency of three dimensional digital media for agricultural subjects. The field experiments were conducted in the experiment farm of Beijing Academy of Agricultural and Forestry Sciences, Beijing City. During maize growth periods of jointing stage, horn mouth stage, florescence stage, grouting stage, maturation period and senility period, SPAD values of maize leaves were obtained by using SPAD-502 and four appearance parameters were collected by an apparent image acquisition system using linear light source. The four appearance parameters were diffuse reflectance, transparency, specular reflectance and roughness. In order to finely model the maize leaf appearance, leaf surface was divided into three structures including mesophyll, main vein and secondary vein. With the data, we analyzed the relationship between SPAD, growth period parameters and various kinds of appearance parameters of the three structures, respectively. We applied a variety of mathematical models to fit the data to describe the potential relationship between SPAD, growth period and diffuse, transparency appearance of mesophyll. Based on R2 and RMSE, we selected quadratic function for diffuse reflectance and negative exponential function for transparency. The coefficients of determination for the RGB channels of final diffuse model of mesophyll were 0.9137, 0.8511, and 0.6146, respectively, and their corresponding values of RMSE were 0.01549, 0.0128, and 0.01533. The coefficients of determination for the transparency model were 0.9975, 0.9913, and 0.9907, respectively, and their corresponding values of RMSE were 0.0064, 0.0096, and 0.0207. Data showed diffuse reflection and transparency of secondary vein could be classified into four sections by SPAD values and there was little difference between each class of appearance values. We used mean values of each section as diffuse and transparency parameters. The diffuse reflection and transparency of main vein has little change in whole growth periods. As such, we used mean values as unique and invariant apparent feature in diffuse and transmission reflection. We found that specular reflectance and roughness parameters of the three structures were not related to SPAD and growth period, therefore, mean values of these two appearance parameters obtained from all samples were used as a glossy appearance. Based on the above analysis, a series of quantitative appearance models for three structures were established respectively with SPAD and growth period as input parameters, and appearance material parameters as output. The spatial-varying appearance of leaf surface formed specific texture patterns. We abstracted the texture pattern and constructed a parameterized structure method to generate a structure texture image for synthesis of the geometric features of mesophyll, main vein and secondary vein structures. Each pixel of structure texture image represented a structure category of the point at this position, and we used the appearance models of each structure to assign appearance parameters for each pixel. Finally, four appearance textures were generated for simulating the appearance patterns of the leaf surface including diffuse texture, transparency texture, specular texture and roughness texture. A real-time rendering framework was developed to simulate the subtle interaction between plant leaves and light. In order to achieve a WYSIWYG display result, we simplified the light computing by decomposing light environment into a directional light source (sun) and some environment light sources (sky). A complete realistic effect was finally developed by combing the appearance model and illumination computation using a deferred lighting rendering framework which can simulate real-time shadows, ambient occlusion, and dynamic radiance from sun and sky. The experimental results demonstrated that the proposed approach was capable of generating different appearance of crop leaves by controlling the agronomic parameters and achieving a visually satisfactory display result. Our method showed diffuse reflectance and transmittance were the most affected by the parameters of SPAD and growth stage, while the specular reflection and roughness parameters were always stable. There were differences in the fitting results of diffuse and transparency model of mesophyll in RGB channels, while simulation result indicated there was little impact on the final visualization effect. Our method has a great potential to become an effective visualization tool for agricultural application, such as crop model, agricultural education system, and digital media for agricultural subjects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Three-dimensional (3D) Plant modeling and visualization is a key research issue in both digital plant and agricultural application. Leaf is one of the vital organs in a plant, so the 3D modeling and shading of plant leaves is an important and fundamental work for achieving the goals of digital plant. Appearance simulation of plant leaves is still a challenging issue because of its intricate underlying structure and complex and subtle interaction with light. Texture mapping using leaf photo is a common method for appearance simulation, however, it could bring noise caused by light environment and camera position in lighting simulation step. This paper presents a technique for simulating the appearance of plant leaves with multiple images. Our method can estimate the spatially-varying reflectance properties of plant leaf surface based on a few images, which capture leaves' appearance transition information with different light directions. An apparent image acquisition system using linear light source is built for capturing 400 images with a fixed camera viewpoint and a single direction of motion for the linear light source. This system is composed of a driving module, a linear source module, a background module and a camera. Using a linear light rather than a point light source as the illuminant, we can obtain a piece of area with more intensive illumination. With these image data, we develop a fitting method, which is able to estimate the diffuse color, specular color and specular roughness of each point on the leaf surface. In our method, the isotropic ward model is utilized as the appearance model for specifying that how the leaf surface reflects light. Our fitting technique first simulates the change of reflectance attributes of diffuse and specular reflectance lobes under moving linear light source. In this process, a rectangle is employed to simulate the linear light source and Monte Carlo integration method is used to calculate the radiation transmission process. When we have the simulating results, the appearance parameters of each pixel are determined by comparing its actual parameter values to the simulating results. By above fitting method, 3 kinds of spatially-varying appearance parameters are saved into 3 parameter images for rendering leaf appearance. For quickly shading, multipoint point light sources are used for simulating various illumination conditions instead of complex radiative transfer integral. Using appearance parameter images and shading method, static appearance or dynamic appearance transition of plant leaves can be generated realistically. From the results obtained by this method, we find that it can render more accurate and real appearance texture of leaves compared to traditional texture mapping methods. The advantages of our method are that the appearance parameter images for rendering have removed the light and viewport noise, and only contained the appearance material information. In order to prove this conclusion, we quantitatively analyze the reason for this advantage by some formula derivations in this paper. But for obtaining these advantages, our method needs more complex data acquisition process and parameter fitting algorithm, which will reduce the efficiency of simulation. For improving the efficiency of our method, 2 approaches are discussed in this paper, including reducing image resolution and fitting the specular parameters of the whole leaf by a few sample points. Our method can estimate some appearance parameters which are plant leaf own intrinsic properties. We believe this characteristic will make these appearance parameters used not only for visualization, but also as some important phenotypes instead of so-called color data. In the future work, we will extend the application of our method in agriculture, such as monitoring plant growth status with the appearance parameters, or analyzing the differences among plant varieties. [ABSTRACT FROM AUTHOR]