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Copyright of Guangdong Medical Journal is the property of Guangdong Medical Journal 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 Journal of Practical Medicine / Shiyong Yixue Zazhi is the property of Journal of Practical Medicine 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 Smart Agriculture is the property of Smart Agriculture 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 Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences 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 Vibration, Measurement & Diagnosis is the property of Nanjing Hangkong Daxue 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 Polymer Materials Science & Engineering is the property of Sichuan University, Polymer Research Institute 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 Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica 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 Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology 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.)

Copyright of Smart Agriculture is the property of Smart Agriculture 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 Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research 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 Modern Food Science & Technology is the property of Editorial Office of Modern Food 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.)

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.)

Due to roots are hidden underground and require considerable effort to characterize, research on plant roots lags behind that of the aboveground organs. This study presented an image-based high-throughput root phenotyping system, which integrated a reliable and simple image acquisition unit and an automatic image analysis software. The hardware platform utilized a blue background cloth to simplify root image content, the tripod was placed in front of the background cloth with a disk marker pasted on its top, root sample bundles with the wishbone of the tripod using a fishing line. A camera (Canon EOS 5D Mark III) with a 28 mm focal length lens faced the background cloth and was focused on the root crown, with aperture-priority model and other settings remain default. Coupled with the optimized image acquisition using the hardware platform, segmentation of the root images from the background required only the Support Vector Machine (SVM) based on the pixel classifying method with a 15-dimensional eigenvector. The contour of a disk marker was roughly spherical, a circularity scoring evaluation function was used to distinguish other compositions according to thresholding of 0.85. Along the stem of the root image, when the width increased up to 1.5 times, the assumed root original point was found. Contaminated data was generated during the sampling process due to root vulnerability nature; a redundant root pruning method was introduced by circular covering beyond 90% of the root area. The root angle of branching was defined as the sum of left and right angles which was calculated by circular covering beyond 90% root area with a 10 cm radius. The convex hull of the root was a polygon, binarization of the root image was the connected domain, the exclusive-or produced gaps within the root region defined as “inner gap” or between the convex and connected domain defined as “gap”. Root uniformity assessment was achieved by evaluating the distance between the center of gravity and the center of the min-enclosure circle of root image pixels. To explain the color difference of roots, introduced 6 channels of Hue Saturation Value (HSV) and Lab color spaces, and a Dark Green Color Index (DGCI) accounted for green degree difference between root images. The software platform was designed to quickly analyze the images acquired using the hardware platform and was to create a simple-to-use and robust program that batch processed a list containing root crown images and output a data file with the measures for each sample in a form convenient for data analysis. A total of 29 phenotyping indices were extracted from each input image, which was stored in a Comma-Separated Values (CSV) text file. Segmented images as well as processed images on which visual depictions of the extracted features were drawn on the intermediate image. Multithread image processing and final phenotyping indices results were visualizations in the computational processing display area. Finally, 29 phenotyping indices of 135 maize inbred lines were available, the preliminary statistical was conducted to describe extreme value distribution. Phenotyping indices measuring accuracy were improved by root angle of branching correlative analysis method between manual measurement and automatic method in this platform, the coefficient of determination between two methods hit 0.85. An unsupervised clustering (K-means) method classified 135 maize inbred lines into 3 classifications, by the aid of a profile chart and root classed results could conclude that phenotyping indices in morphological characters, color features, and spatial arrangement played an important role in the identification of different types. The platform demonstrated in this study that made the high-throughput maize root phenotyping come to be true, with the help of further researches such as selecting a high nitrogen-use efficiency plant according to root phenotyping indices could be done. [ABSTRACT FROM AUTHOR]

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 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.)

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.)

The phenotypic traits of corn ear are important quantitative data in maize breeding and variety identification. In tradition, breeding workers are employed to deal with lots of corn ears by means of manual measurement and visual count, however this process is seriously labor-consuming and time-costing, and the measured traits are prone to be subjective and incomplete. In recent years, some semi-automatic systems based on machine vision and image analysis have been developed and applied to the maize variety test, however fully automated test system is still a challenge task owing to the strict high-throughput and high-precision requirements in large-scale maize breeding. To balance efficiency and accuracy of variety test for corn ears, in this paper, a high-throughput phenotypic measurement method and system based on panoramic surface image is proposed. Firstly, a novel mechanic system is proposed, which automatically conveys corn ears above a chain-roller structure, while the rolling corn ears are continuously imaged by a fixed industrial camera that is perpendicular to the moving plane of corn ear. In only several seconds, dozens of side images in which corn ears are in different positions can be collected to generate the image dataset of single corn ear. By analyzing the movement state of corn ear, a transformation model which describes the relationship among ear roll, camera imaging and surface position is then built to bridge the image sequence and the panoramic surface image of corn ear. Corn ears in the image sequence are respectively segmented and the center axes are dynamically determined by figuring out the shape and bounding box. This model always extracts the most appropriate sub regions of corn ear from image sequence, and then stitches them to the calculated positions on the panoramic surface image. As a result, the panoramic image of corn ear demonstrates the three-dimensional surface information in a two-dimensional image, and thus provides more intuitive and complete way for phenotyping calculation of corn ear. The valid surface region of corn ear in the panoramic image is further determined by the boundary detection technique that is performed by evaluating the perimeters of corn ear in the image sequence. Robust kernel segmentation based on hierarchical threshold method is also utilized to extract all candidate kernels which satisfy area and shape constraint, and some more restrictive filters based on machine learning methods, such as SVM (support vector machine), can also be taken to evaluate the validation of kernels. The segmented kernels in the panoramic image are used to calculate the total kernels, number of ear rows and kernels per row. The experimental results show that the proposed method and system can achieve optimized efficiency and accuracy balance. High-throughput convey mechanism improves the efficiency of image acquisition to 15 ears per minute. Compared with the methods based on single and multiple images, the variety test method based on panoramic surface image can make full use of the entire surface information of corn ear and reveal its individual phenotypic traits. The computation accuracies of ear length, ear diameter, number of ear rows, kernels per row and total kernels are up to 99%, 91.84%, 97.15%, 98.89% and 95.37% respectively. [ABSTRACT FROM AUTHOR]

In situ measurements of leaf area index (LAI) are needed for monitoring crop growth conditions at the site level. In this study, we investigated the use of an infrared digital camera for canopy structural information extraction. Firstly, the camera (JAI industry camera, model AD-080CL, resolution: 1024×768, sensor size: 1/3 CCD, FPS: 30, focal length: 12 mm) was placed 6 m above the canopy, which was equipped with an infrared/color imaging switcher. Digital images were taken every 30 mins from above the canopy looking downward vertically. A Gaussian distribution based threshold technique was used to separate green vegetation tissues from background soil and residue materials in order to derive the canopy vertical gap fraction from the digital photos. The distribution of intensity for the background pixels was narrow compared to that of green plant pixels, The rising side of the background elements distribution on the histogram could be well-fitted with a Gaussian distribution function. However, the falling side overlapped the rising side of the green leaf distribution, which made it deviate from a Gaussian distribution at the junction of the two population distributions, the intensity of shaded leaves was generally smaller than that of sunlit leaves, which may cause confusion between shaded leaves and background elements. In this process, one of the challenges was how to overcome various natural light conditions which sometimes strongly affect the profile of the crop images taken from outdoor. In order to eliminate ambiguity which sometimes strongly affects the segmentation of the crop images taken outdoor, we adopted infrared images instead of color images, reflectance of healthy green plants in infrared images (760 nm) was higher than soil background due to red-edge phenomenon, which enhanced the intensity of shaded leaves. The intensity valueμin histogram represents the value at which the frequency of the background elements was maximal (the first peak from the left side of the histogram), and TL represented the intensity value below which the proportion of pixels was not more than 2.5% of the total number of pixels. The threshold TR was the mirror of TL with respect toμ, assuming a Gaussian distribution for background pixels. The use of 2.5% to determine the width of distribution of background pixels was to exclude pixels with exceptional low intensity while keeping the error below an acceptable level. In canopy structure parameter analysis phase, assuming the foliage was azimuthally uniform and spatially randomly distributed, the relationship between canopy gap fraction and LAI was given by the Poisson distribution. A spherical leaf inclination distribution function (LIDF) was considered a good first-order approximation for crop canopies, in which case foliage projection coefficient for the plane perpendicular to solar zenith angle was equal to 0.5 at any direction. LAI was estimated from the vertical gap fraction obtained from digital photography looking vertically downward. The experiment was conducted in 2016, two varieties in different decades (60s and 90s), density was 45000 plants/ha, normal water and fertilizer management. The LAI of two varieties varied greatly during the whole growth period, especially after grain filling, LAI of 60s variety decreased rapidly, however modern variety had an excellent "stay-green" characteristic, which maintained LAI at a certain high extent. A performance comparison of AccuPAR and our method was carried out, 18 samples were collected with two methods, the results showed that the LAI obtained using digital photography was linearly related with the AccuPAR method, the coefficient of determination between two methods hit 0.94. Our method provides a whole growth period automatic LAI monitoring solution. [ABSTRACT FROM AUTHOR]

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.)

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 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 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.)

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 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.)

Plant phenotype is a key link in understanding gene function and environmental effects, and with the demand of further research of plant function genomics, the traditional phenotypic observation has become the main bottleneck. High-throughput plant phenome analysis technology such as image processing is an effective way to solve this problem. In this paper, a maize plant drought stress phenotype testing method was proposed based on the time-series images. Firstly, an acquisition system of time-series images was developed. System hardware included the mvc3000 high speed industrial camera (the maximum resolution 1600×1200 pixels, 24 FPS (frames per second)), the Pentax len (8.5 mm, f/1.5), the storage card (32G), the holder and the background. Software development environment included Win7 operating system, Microsoft Visual Studio 2010 Professional, and OpenCV2.4.3. Maize plant time-series images were acquired from 08:00 to 17:00 every 10 mins, and various natural light conditions strongly affected the profile of the crop images taken outdoors. This often made it rather difficult for vegetation parts segmented from the background parts in an image. We adopted the K-means and SVM (support vector machine) algorithm to create a classifier used in rough segmentation, and the classifier was composed of 24 dimension feature vectors including color features, texture features, and gradient features. The result refinement was implemented with a non-local mean (NLM) filter, and actually the anisotropic structural-aware filter was adopted to implement the NLM function, in which the pixels were reinforced with each other in the same structure and even if they were not the first-order neighboring pixels. Secondly, the plant organs such as stem and leaf were segmented in binary image with a scan line method, which used the plant morphology characteristics such as alternate phyllotaxis, curved blade, and leaf margin in folds as prior knowledge. The method denoted the pixels according to the number of neighborhoods. Specifically, each row of pixels in the scan line was marked as a candidate region, and the candidate regions were labeled as the original organ or new organ according to the number of neighborhoods. When all of the candidate regions were labeled, a cluster of regions which presented rectangular shape were defined as plant stem. A labeled cluster of leaf would be merged to the other cluster if they shared neighborhood regions up to 70%. Finally, the ratio SOP1/SOP2 was used to characterize the drought stress response. SOP is the intersection point of stem and leaf, SOP is a point in stem line, LEN is the length of leaf tip to stem line, SOP1 is a point in leaf curve with length 1/4LEN, SOP2 is a point in leaf curve with length 2/3 LEN. SOP1/SOP2 represented the degree of leaf bending. It was comparable even between the different leaf orders, and had strong robustness to environmental factor disturbance. The field tests showed that the SOP1/SOP2 of drought-resistant genetically-modified maize presented an ascending trend before midday and a descending trend after then, which could be caused by transpiration leading to leaf water loss. The change trend of environmental factors such as air relative humidity against SOP1/SOP2 showed that under drought stress condition, genetically-modified maize was more sensitive to moisture content of the atmosphere, and the test on conventional maize plant showed that the SOP1/SOP2 lacked the regularity with the time. [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.)

Phenotypic traits of corn ears are important parameters for maize breeding and production forecast. A phenotypic computation method based on distribution map of kernels for corn ears is presented to comprehensively analyze the geometrical, quantitative, color and texture traits of corn ear and its kernels. A phenotypic detection system of corn ears, which consists of stepping motor, charge coupled device (CCD) camera, light-emitting diode (LED) back lights, image acquisition card and semiclosed box, is designed to capture main side images of corn ear. Corn ear is fixed vertically on a turn table driven by stepping motor, and thus image sequence of corn ear can be captured from designated angles and covers the entire surface of corn ear. In this study, 4 orthotropic images are captured to build three-dimensional reference frame of corn ear. Firstly, axial and radial distortion corrections are successively applied to image sequences and generate standard image sequence of corn ear. Therein, axial distortion correction regularizes the heights of corn ear for image sequence to their average height, and radial distortion correction is used to recover size and shape of kernels on the surface of corn ear, since those kernels lie on border regions of corn ears which have obvious distortion. A dedicated image segmentation method, which has utilized geometrical and color traits of kernels of corn ear to the greatest extent, is then used to extract all kernels of corn ear from corrected image sequence. Meanwhile, contour lines and split lines of corn ear in image sequence are calculated based on pixel scale, and used to generate the mapping relationship among the images of corn ear. Contour lines of corn ear can be used to generate a three-dimensional surface model by transforming the coordinates of lines from two-dimensional to three-dimensional space, and then the three-dimensional model of corn ear is used to calculate geometrical traits, e.g.perimeter, surface and volume.Moreover, contour lines of corn ear can also output corresponding split lines which split each corn image into different regions, and further classify kernels into different types according to position relationship between kernels and split lines.On the basis of kernel scale, the classified kernels from segmented image sequence can be assembled together to generate a distribution map which describes entire surface kernels of corn ear. According to the distribution map of kernels and segmented image sequence, the quantitative and geometrical traits of kernels, such as rows per ear, kernels per row, total kernels and kernel thickness can be accurately calculated using Delaunay and BellmanFord methods.The proposed method and system can simultaneously detect multiple types of phenotypic traits from image sequence of corn ear, and have higher accuracy in almost all phenotypic traits than the detection method based on single profile image of corn ear.Experimental results demonstrate that the computed traits have good consistence with the observed values, and the average computation accuracy of main traits, i.e.rows per ear, kernels per row, total kernels, ear length and ear diameter, can respectively reach 98.231%, 94.351%, 96.921%, 98.956% and 98.165%.Thus, the proposed method can be applied for precise phenotypic detection and breeding of corn ears. [ABSTRACT FROM AUTHOR]

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]

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