Journal articles: 'Plant 3D reconstruction'

1

Ando, Ryuhei, Yuko Ozasa, and Wei Guo. "Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds." Plant Phenomics 2021 (April 2, 2021): 1–15. http://dx.doi.org/10.34133/2021/3184185.

Full text

Abstract:

The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.

APA, Harvard, Vancouver, ISO, and other styles

2

Wang, Jizhang, Yun Zhang, and Rongrong Gu. "Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction." Agriculture 10, no. 10 (October 8, 2020): 462. http://dx.doi.org/10.3390/agriculture10100462.

Full text

Abstract:

Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies. To promote the development of plant canopy structure measurement based on 3D reconstruction, we reviewed the latest research progress achieved using visual sensors to measure the 3D plant canopy structure from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction based on 3D reconstruction, and the conclusion and promise of plant canopy measurement technology. In the current research phase on 3D structural plant canopy measurement techniques, the leading algorithms of every step for plant canopy structure measurement based on 3D reconstruction are introduced. Finally, future prospects for a standard phenotypical analytical method, rapid reconstruction, and precision optimization are described.

APA, Harvard, Vancouver, ISO, and other styles

3

Yin, Kangxue, Hui Huang, Pinxin Long, Alexei Gaissinski, Minglun Gong, and Andrei Sharf. "Full 3D Plant Reconstruction via Intrusive Acquisition." Computer Graphics Forum 35, no. 1 (August 25, 2015): 272–84. http://dx.doi.org/10.1111/cgf.12724.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Yang, Myongkyoon, and Seong-In Cho. "High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning." Agriculture 11, no. 10 (October 15, 2021): 1010. http://dx.doi.org/10.3390/agriculture11101010.

Full text

Abstract:

Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant (Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.

APA, Harvard, Vancouver, ISO, and other styles

5

Sun, Guoxiang, and Xiaochan Wang. "Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration." Agronomy 9, no. 10 (September 28, 2019): 596. http://dx.doi.org/10.3390/agronomy9100596.

Full text

Abstract:

Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements of the 3D morphological data of greenhouse plants, it is necessary to frequently adjust the relative position between the sensor and the plant. Therefore, it is necessary to frequently adjust the Kinect sensor position and consequently recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process. A high-throughput 3D rapid greenhouse plant point cloud reconstruction method based on autonomous Kinect v2 sensor position calibration is proposed for 3D phenotyping greenhouse plants. Two red–green–blue–depth (RGB-D) images of the turntable surface are acquired by the Kinect v2 sensor. The central point and normal vector of the axis of rotation of the turntable are calculated automatically. The coordinate systems of RGB-D images captured at various view angles are unified based on the central point and normal vector of the axis of the turntable to achieve coarse registration. Then, the iterative closest point algorithm is used to perform multiview point cloud precise registration, thereby achieving rapid 3D point cloud reconstruction of the greenhouse plant. The greenhouse tomato plants were selected as measurement objects in this study. Research results show that the proposed 3D point cloud reconstruction method was highly accurate and stable in performance, and can be used to reconstruct 3D point clouds for high-throughput plant phenotyping analysis and to extract the morphological parameters of plants.

APA, Harvard, Vancouver, ISO, and other styles

6

Hartley, Zane K. J., Aaron S. Jackson, Michael Pound, and Andrew P. French. "GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit." Plant Phenomics 2021 (October 8, 2021): 1–11. http://dx.doi.org/10.34133/2021/9874597.

Full text

Abstract:

3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.

APA, Harvard, Vancouver, ISO, and other styles

7

Schunck, David, Federico Magistri, Radu Alexandru Rosu, André Cornelißen, Nived Chebrolu, Stefan Paulus, Jens Léon, et al. "Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis." PLOS ONE 16, no. 8 (August 18, 2021): e0256340. http://dx.doi.org/10.1371/journal.pone.0256340.

Full text

Abstract:

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

APA, Harvard, Vancouver, ISO, and other styles

8

Wang, Feiyi, Xiaodan Ma, Meng Liu, and Bingxue Wei. "Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits." Agronomy 12, no. 3 (March 12, 2022): 692. http://dx.doi.org/10.3390/agronomy12030692.

Full text

Abstract:

Precise reconstruction of the morphological structure of the soybean canopy and acquisition of plant traits have great theoretical significance and practical value for soybean variety selection, scientific cultivation, and fine management. Since it is difficult to obtain all-around information on living plants with traditional single or binocular machine vision, this paper proposes a three-dimensional (3D) method of reconstructing the soybean canopy for calculation of phenotypic traits based on multivision. First, a multivision acquisition system based on the Kinect sensor was constructed to obtain all-around point cloud data of soybean in three viewpoints, with different fertility stages of soybean as the research object. Second, conditional filtering and K-nearest neighbor filtering (KNN) algorithms were used to preprocess the raw 3D point cloud. The point clouds were matched and fused by the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms to accomplish the 3D reconstruction of the soybean canopy. Finally, the plant height, leafstalk angle and crown width of soybean were calculated based on the 3D reconstruction of soybean canopy. The experimental results showed that the average deviations of the method was 2.84 cm, 4.0866° and 0.0213 m, respectively. The determination coefficients between the calculated values and measured values were 0.984, 0.9195 and 0.9235. The average deviation of the RANSAC + ICP was 0.0323, which was 0.0214 lower thanthe value calculated by the ICP algorithm. The results enable the precise 3D reconstruction of living soybean plants and quantitative detection for phenotypic traits.

APA, Harvard, Vancouver, ISO, and other styles

9

Fang, Wei, Hui Feng, Wanneng Yang, Lingfeng Duan, Guoxing Chen, Lizhong Xiong, and Qian Liu. "High-throughput volumetric reconstruction for 3D wheat plant architecture studies." Journal of Innovative Optical Health Sciences 09, no. 05 (July 18, 2016): 1650037. http://dx.doi.org/10.1142/s1793545816500371.

Full text

Abstract:

For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08[Formula: see text]cm with an average plant height of 40.07[Formula: see text]cm) and 10.06% (1.41[Formula: see text]g with an average plant fresh weight of 14.06[Formula: see text]g), respectively. The root mean square error (RMSE) was 1.37[Formula: see text]cm and 1.79[Formula: see text]g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20[Formula: see text]s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform.

APA, Harvard, Vancouver, ISO, and other styles

10

Zaman, Sharifa, and B. Fatima. "THREE-DIMENSIONAL RECONSTRUCTION AND VISUALIZATION OF PLANT CELLS." Journal of Mountain Area Research 5 (December 29, 2020): 28. http://dx.doi.org/10.53874/jmar.v5i0.80.

Full text

Abstract:

The mechanical properties (like sensory texture etc.) of plants/fruits directly depend on their microstructures. Therefore, it is very important to well understand the geometry and topology of cells in order to control the microstructure for better mechanical response. In this research, techniques of digital image processing and segmentation in conjunction with mathematical morphology models are used to visualize and analyze the 3D cells of potato. ImageJ and MATLAB are used throughout in this study. The labeled image stacks are essential for studying quantitative characterization of 3D cells, MATLAB is used to label each image stacks. By using MATLAB 12420 cells were segmented within a short period of time and labeled each cell uniquely.

APA, Harvard, Vancouver, ISO, and other styles

11

Guan, Haiou, Meng Liu, Xiaodan Ma, and Song Yu. "Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis." Remote Sensing 10, no. 8 (August 1, 2018): 1206. http://dx.doi.org/10.3390/rs10081206.

Full text

Abstract:

Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R2) of 0.9890 for the estimation of soybean height and a R2 of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R2 of 0.9936 for the estimation of soybean height and a R2 of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions.

APA, Harvard, Vancouver, ISO, and other styles

12

Gibbs, Jonathon A., Michael P. Pound, Andrew P. French, Darren M. Wells, Erik H. Murchie, and Tony P. Pridmore. "Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 6 (November 1, 2020): 1907–17. http://dx.doi.org/10.1109/tcbb.2019.2896908.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Yongjian, Weiliang Wen, Sheng Wu, Chuanyu Wang, Zetao Yu, Xinyu Guo, and Chunjiang Zhao. "Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates." Remote Sensing 11, no. 1 (December 31, 2018): 63. http://dx.doi.org/10.3390/rs11010063.

Full text

Abstract:

High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.

APA, Harvard, Vancouver, ISO, and other styles

14

Serra, Leo, Sovanna Tan, Sarah Robinson, and Jane A. Langdale. "Flip-Flap: A Simple Dual-View Imaging Method for 3D Reconstruction of Thick Plant Samples." Plants 11, no. 4 (February 13, 2022): 506. http://dx.doi.org/10.3390/plants11040506.

Full text

Abstract:

Plant development is a complex process that relies on molecular and cellular events being co-ordinated in space and time. Microscopy is one of the most powerful tools available to investigate this spatiotemporal complexity. One step towards a better understanding of complexity in plants would be the acquisition of 3D images of entire organs. However, 3D imaging of intact plant samples is not always simple and often requires expensive and/or non-trivial approaches. In particular, the inner tissues of thick samples are challenging to image. Here, we present the Flip-Flap method, a simple imaging protocol to produce 3D images of cleared plant samples at the organ scale. This method allows full 3D reconstruction of plant organs suitable for 3D segmentation and further related analysis and can be easily handled by relatively inexperienced microscopists.

APA, Harvard, Vancouver, ISO, and other styles

15

Martinez-Guanter, Jorge, Ángela Ribeiro, Gerassimos G. Peteinatos, Manuel Pérez-Ruiz, Roland Gerhards, José María Bengochea-Guevara, Jannis Machleb, and Dionisio Andújar. "Low-Cost Three-Dimensional Modeling of Crop Plants." Sensors 19, no. 13 (June 28, 2019): 2883. http://dx.doi.org/10.3390/s19132883.

Full text

Abstract:

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.

APA, Harvard, Vancouver, ISO, and other styles

16

Gibbs, Jonathon A., Michael Pound, Andrew P. French, Darren M. Wells, Erik Murchie, and Tony Pridmore. "Approaches to three-dimensional reconstruction of plant shoot topology and geometry." Functional Plant Biology 44, no. 1 (2017): 62. http://dx.doi.org/10.1071/fp16167.

Full text

Abstract:

There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements.

APA, Harvard, Vancouver, ISO, and other styles

17

Wu, Jingwen, Xinyu Xue, Songchao Zhang, Weicai Qin, Chen Chen, and Tao Sun. "Plant 3D reconstruction based on LiDAR and multi-view sequence images." International Journal of Precision Agricultural Aviation 1, no. 1 (2018): 37–43. http://dx.doi.org/10.33440/j.ijpaa.20180101.0007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Zechmann, Bernd, and Günther Zellnig. "3D Reconstruction of Plant Leaf Cells Using TEM and FIB-SEM." Microscopy and Microanalysis 28, S1 (July 22, 2022): 1098–99. http://dx.doi.org/10.1017/s1431927622004640.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Ni, Zhijiang, Thomas Burks, and Won Lee. "3D Reconstruction of Plant/Tree Canopy Using Monocular and Binocular Vision." Journal of Imaging 2, no. 4 (September 29, 2016): 28. http://dx.doi.org/10.3390/jimaging2040028.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Liu, Suxing, Lucia Acosta-Gamboa, Xiuzhen Huang, and Argelia Lorence. "Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping." Journal of Imaging 3, no. 3 (September 18, 2017): 39. http://dx.doi.org/10.3390/jimaging3030039.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Garrido, Miguel, Dimitris Paraforos, David Reiser, Manuel Vázquez Arellano, Hans Griepentrog, and Constantino Valero. "3D Maize Plant Reconstruction Based on Georeferenced Overlapping LiDAR Point Clouds." Remote Sensing 7, no. 12 (December 17, 2015): 17077–96. http://dx.doi.org/10.3390/rs71215870.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Liu, Ke Nan, Qi Liang Yang, Ying Jie Sun, Zhen Yang Ge, and Yan Zhang. "3D Reconstruction of Jatropha curcas L. Root Based on Image." Advanced Materials Research 524-527 (May 2012): 3900–3906. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3900.

Full text

Abstract:

Because the plant roots are in underground, hard observation and measurement, the simulation and 3 D reconstruction of the plant roots is always a difficulty of virtual plant. This paper based on image modeling technology, according to different viewpoint in space in the distribution of the sparse photo stems and roots corresponding stems the corresponding relation of solving the halfway point, to rebuild 3 D model of the plant roots.Targeted at the root of Jatropha Curcas, the experimental results show that the method is feasible and effective, and can provide reference for the establishment of a complete plant visualization system.

APA, Harvard, Vancouver, ISO, and other styles

23

Gao, Tian, Feiyu Zhu, Puneet Paul, Jaspreet Sandhu, Henry Akrofi Doku, Jianxin Sun, Yu Pan, Paul Staswick, Harkamal Walia, and Hongfeng Yu. "Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants." Remote Sensing 13, no. 11 (May 27, 2021): 2113. http://dx.doi.org/10.3390/rs13112113.

Full text

Abstract:

The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.

APA, Harvard, Vancouver, ISO, and other styles

24

Sun, Guoxiang, Xiaochan Wang, Ye Sun, Yongqian Ding, and Wei Lu. "Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants." Sensors 19, no. 15 (July 30, 2019): 3345. http://dx.doi.org/10.3390/s19153345.

Full text

Abstract:

Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.

APA, Harvard, Vancouver, ISO, and other styles

25

Lepej, P., M. Lakota, and J. Rakun. "Robotic real-time 3D object reconstruction using multiple laser range finders." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 183–88. http://dx.doi.org/10.1017/s2040470017001157.

Full text

Abstract:

An accurate 3D model of an outdoor scene can be used in many different scenarios of precision agriculture, for instance to analyse the silhouette of a tree crown canopy for precision spraying, to count fruit for fruit yield prediction or to simply navigate a vehicle between the plant rows. Instead of using stereovision, limited by the problems of different light intensities, or by using expensive multi-channel 3D range finder (LIDAR scanner), limited by the number of channels, this work investigates the possibility of using two single channel LIDAR scanners mounted on a small robot to allow a real-time 3D object reconstruction of the robot environment. The approach used readings captured by two LIDAR scanners, SICK LMS111 and SICK TiM310, where the first one was scanning horizontally and the second one vertically. In order to correctly map the 3D points of the readings from the vertical sensor into a 3D space, a custom SLAM algorithm based on image registration techniques was used to calculate the new positions of the robot. The approach was tested in an indoor and outdoor environment, proving its accuracy with an error rate of 0.02 m±0.02 m for vertical and −0.01 m±0.13 m for the horizontal plane.

APA, Harvard, Vancouver, ISO, and other styles

26

Ma, Xiaodan, Kexin Zhu, Haiou Guan, Jiarui Feng, Song Yu, and Gang Liu. "Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies." Sensors 19, no. 5 (March 8, 2019): 1201. http://dx.doi.org/10.3390/s19051201.

Full text

Abstract:

A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R2 were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.

APA, Harvard, Vancouver, ISO, and other styles

27

Sonohat, G., H. Sinoquet, V. Kulandaivelu, D. Combes, and F. Lescourret. "Three-dimensional reconstruction of partially 3D-digitized peach tree canopies." Tree Physiology 26, no. 3 (March 1, 2006): 337–51. http://dx.doi.org/10.1093/treephys/26.3.337.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Yang, Yuhang, Jinqian Zhang, Kangjie Wu, Xixin Zhang, Jun Sun, Shuaibo Peng, Jun Li, and Mantao Wang. "3D Point Cloud on Semantic Information for Wheat Reconstruction." Agriculture 11, no. 5 (May 16, 2021): 450. http://dx.doi.org/10.3390/agriculture11050450.

Full text

Abstract:

Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction technology, the reconstructed wheat 3D model can also be used for phenotypic analysis. In this paper, a method is proposed to reconstruct wheat 3D model based on semantic information. The method can generate the corresponding 3D point cloud model of wheat according to the semantic description. First, an object detection algorithm is used to detect the characteristics of some wheat phenotypes during the growth process. Second, the growth environment information and some phenotypic features of wheat are combined into semantic information. Third, text-to-image algorithm is used to generate the 2D image of wheat. Finally, the wheat in the 2D image is transformed into an abstract 3D point cloud and obtained a higher precision point cloud model using a deep learning algorithm. Extensive experiments indicate that the method reconstructs 3D models and has a heuristic effect on phenotypic analysis and breeding research by deep learning.

APA, Harvard, Vancouver, ISO, and other styles

29

Gong, Y., Y. Yang, and X. Yang. "THREE-DIMENSIONAL RECONSTRUCTION OF THE VIRTUAL PLANT BRANCHING STRUCTURE BASED ON TERRESTRIAL LIDAR TECHNOLOGIES AND L-SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 403–10. http://dx.doi.org/10.5194/isprs-archives-xlii-3-403-2018.

Full text

Abstract:

For the purpose of extracting productions of some specific branching plants effectively and realizing its 3D reconstruction, Terrestrial LiDAR data was used as extraction source of production, and a 3D reconstruction method based on Terrestrial LiDAR technologies combined with the L-system was proposed in this article. The topology structure of the plant architectures was extracted using the point cloud data of the target plant with space level segmentation mechanism. Subsequently, L-system productions were obtained and the structural parameters and production rules of branches, which fit the given plant, was generated. A three-dimensional simulation model of target plant was established combined with computer visualization algorithm finally. The results suggest that the method can effectively extract a given branching plant topology and describes its production, realizing the extraction of topology structure by the computer algorithm for given branching plant and also simplifying the extraction of branching plant productions which would be complex and time-consuming by L-system. It improves the degree of automation in the L-system extraction of productions of specific branching plants, providing a new way for the extraction of branching plant production rules.

APA, Harvard, Vancouver, ISO, and other styles

30

Putro, A. W., A. P. Nugroho, L. Sutiarso, and T. Okayasu. "Application of 3D reconstruction system based on close-range photogrammetry method for plant growth estimation." IOP Conference Series: Earth and Environmental Science 1038, no. 1 (June 1, 2022): 012051. http://dx.doi.org/10.1088/1755-1315/1038/1/012051.

Full text

Abstract:

Abstract Plant growth monitoring is an important aspect of precision agriculture implementation. The monitoring can be performed by estimating the volume by the result of Three-dimensional (3D) reconstruction by using Close-range Photogrammetry. However, to present the functionality of the system for plant growth behavior, it is necessary to evaluate its accuracy and performance. In this study, a plant growth estimation system based on non-contact measurement using the Close-Range Photogrammetry (CRP) method for volume estimation of Chinese cabbage was developed to measure the growth. The objective of this study was to apply a 3D reconstruction system using the CRP method for validating volume variation and estimate the rate of growth of Chinese cabbage. This system consists of Canon 700D’s DSLR camera and camera stabilizer. The stage of image processing using 3DF’s Zephyr Pro photogrammetric software for generating 3D models. For the validation purposes and its functionality for modelling and estimating volumetric objects, Chinese cabbage with four size variations at different ages was used (14, 21, 28, and 35 Days After Transplant). As the result, the developed system could observe and generate the plant in a three-dimensional manner resemble the actual plant model. Farther the volumetric validation could be obtained with the result of R2 of 0,9991, Root Mean Square Error (RMSE) = 26,08 cm3 and Mean Absolute Percentage Error (MAPE) = 10,43%. From the result, the system could be used for generating plants into 3D objects and its accuracy of measurement is quite good. Further improvements in accuracy need to be made for precise measurements as well as validation for other crop types.

APA, Harvard, Vancouver, ISO, and other styles

31

Sun, Guoxiang, Yongqian Ding, Xiaochan Wang, Wei Lu, Ye Sun, and Hongfeng Yu. "Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging." Sensors 19, no. 23 (December 1, 2019): 5295. http://dx.doi.org/10.3390/s19235295.

Full text

Abstract:

Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

APA, Harvard, Vancouver, ISO, and other styles

32

Roscher, Ribana, Jan Behmann, Anne-Katrin Mahlein, Jan Dupuis, Heiner Kuhlmann, and Lutz Plümer. "DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 89–96. http://dx.doi.org/10.5194/isprsannals-iii-7-89-2016.

Full text

Abstract:

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

APA, Harvard, Vancouver, ISO, and other styles

33

Roscher, Ribana, Jan Behmann, Anne-Katrin Mahlein, Jan Dupuis, Heiner Kuhlmann, and Lutz Plümer. "DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 89–96. http://dx.doi.org/10.5194/isprs-annals-iii-7-89-2016.

Full text

Abstract:

We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.

APA, Harvard, Vancouver, ISO, and other styles

34

Wang, Yinghua, Songtao Hu, He Ren, Wanneng Yang, and Ruifang Zhai. "3DPhenoMVS: A Low-Cost 3D Tomato Phenotyping Pipeline Using 3D Reconstruction Point Cloud Based on Multiview Images." Agronomy 12, no. 8 (August 8, 2022): 1865. http://dx.doi.org/10.3390/agronomy12081865.

Full text

Abstract:

Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a 3D phenotyping pipeline, 3DPhenoMVS, to calculate 17 phenotypic traits of tomato plants covering the whole life cycle. Among all the phenotypic traits, six of them were used for accuracy evaluation because the true values can be generated by manual measurements, and the results showed that the R2 values between the phenotypic traits and the manual ones ranged from 0.72 to 0.97. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during seven growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher biomass and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato and other plants, and the generalization test was also complemented on other six species, which demonstrated that the proposed pipeline will benefit plant breeding, cultivation research, and functional genomics in the future.

APA, Harvard, Vancouver, ISO, and other styles

35

Cai, Dongna, Zhi Li, and Yongjian Huai. "3D Reconstruction and Visual Simulation of Double-Flowered Plants Based on Laser Scanning." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1955013. http://dx.doi.org/10.1142/s0218001419550139.

Full text

Abstract:

Flower plants have become a major difficulty in virtual plant research because of their rich external morphological structure and complex physiological processes. Computer vision simulation provides powerful tools for exploring powerful biological systems and operating laws. In this paper, Chrysanthemum and Chinese rose, double flowers as the symbolic flowers of Beijing, are chosen as the study subject. On the basis of maximizing the protection of flower growth structure, an effective method based on laser scanning for three-dimensional (3D) reconstruction and visual simulation of flower plants is proposed. This method uses laser technology to scan the sample and store it as point cloud data. After applying a series of image analysis and processing techniques such as splicing, denoising, repairing and color correction, the digital data optimized by the sample is obtained accurately and efficiently, and a highly realistic 3D simulation model of the plant is formed. The results of the research indicate that it is a convenient research method for the 3D reconstruction of flower plants and computer vision simulation of virtual plants. It also provides an effective way for in-depth study of scientific experiments and digital protection of rare and endangered plants.

APA, Harvard, Vancouver, ISO, and other styles

36

Xia, Y., J. Tian, P. d’Angelo, and P. Reinartz. "TREE DROUGHT STRESS DETECTION BASED ON 3D MODELLING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 205–10. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-205-2019.

Full text

Abstract:

Abstract. Precise and detailed reconstruction of 3D plant models is an important goal in computer vision. Based on these models, important parameters can be extracted, which would be very useful for monitoring the tree health situation. This paper has firstly constructed the 3D plant model based on MC-CNN using close-range photogrammetric imagery, and then applied a leaf index based segmentation to highlight the leaves region. In the end, the 3D model of each leaf can be represented and some geometric parameters of the leaf are designed and analyzed to predict the drought status. The experiments on real close-range stereo imagery justified the performance of the proposed approach to differentiate drought and healthy leaves.

APA, Harvard, Vancouver, ISO, and other styles

37

Lyu, Weifeng, Wenwang Ran, Jie Liu, Jingyu Zhang, Shaohua Tang, and Jun Xiong. "Development and Validation of Rapid 3D Radiation Field Evaluation Technique for Nuclear Power Plants." Science and Technology of Nuclear Installations 2020 (June 3, 2020): 1–7. http://dx.doi.org/10.1155/2020/8815650.

Full text

Abstract:

Rapid 3D radiation field evaluation is the key point of occupational dose optimization for design and operation of nuclear power plant. Based on the requirement analysis from designers and operators of nuclear power plant, three key technical issues are identified and solved through the development of the RPOS system, which are rapid calculation of 3D radiation field, reconstruction of the calculated 3D radiation field based on measured data, and occupational dose optimization based on 3D radiation field. Operational measurements of dose rate from in-service nuclear power plants are used to test the RPOS system, which shows that accurate 3D radiation field can be rapidly generated by the RPOS system and effectively used on the occupational dose optimization for on-site workers. The applications of the established rapid 3D radiation field evaluation technique on HPR1000 unit design provide evidence on its feasibility in a large scale, the improvement of radiation protection design efficiency and the enhancement of ALARA assessment and justification for nuclear power plants.

APA, Harvard, Vancouver, ISO, and other styles

38

Kim, Hyungki, Changmo Yeo, Inhwan Dennis Lee, and Duhwan Mun. "Deep-learning-based retrieval of piping component catalogs for plant 3D CAD model reconstruction." Computers in Industry 123 (December 2020): 103320. http://dx.doi.org/10.1016/j.compind.2020.103320.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Lati, Ran Nisim, Sagi Filin, and Hanan Eizenberg. "Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points." Precision Agriculture 14, no. 6 (May 23, 2013): 586–605. http://dx.doi.org/10.1007/s11119-013-9317-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

van der Heijden, Gerie, Yu Song, Graham Horgan, Gerrit Polder, Anja Dieleman, Marco Bink, Alain Palloix, Fred van Eeuwijk, and Chris Glasbey. "SPICY: towards automated phenotyping of large pepper plants in the greenhouse." Functional Plant Biology 39, no. 11 (2012): 870. http://dx.doi.org/10.1071/fp12019.

Full text

Abstract:

Most high-throughput systems for automated plant phenotyping involve a fixed recording cabinet to which plants are transported. However, important greenhouse plants like pepper are too tall to be transported. In this research we developed a system to automatically measure plant characteristics of tall pepper plants in the greenhouse. With a device equipped with multiple cameras, images of plants are recorded at a 5 cm interval over a height of 3 m. Two types of features are extracted: (1) features from a 3D reconstruction of the plant canopy; and (2) statistical features derived directly from RGB images. The experiment comprised 151 genotypes of a recombinant inbred population of pepper, to examine the heritability and quantitative trait loci (QTL) of the features. Features extracted from the 3D reconstruction of the canopy were leaf size and leaf angle, with heritabilities of 0.70 and 0.56 respectively. Three QTL were found for leaf size, and one for leaf angle. From the statistical features, plant height showed a good correlation (0.93) with manual measurements, and QTL were in accordance with QTL of manual measurements. For total leaf area, the heritability was 0.55, and two of the three QTL found by manual measurement were found by image analysis.

APA, Harvard, Vancouver, ISO, and other styles

41

Xia, Y., J. Tian, P. d’Angelo, and P. Reinartz. "DENSE MATCHING COMPARISON BETWEEN CENSUS AND A CONVOLUTIONAL NEURAL NETWORK ALGORITHM FOR PLANT RECONSTRUCTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2 (May 28, 2018): 303–9. http://dx.doi.org/10.5194/isprs-annals-iv-2-303-2018.

Full text

Abstract:

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

APA, Harvard, Vancouver, ISO, and other styles

42

Wang, Jinpeng, Haotian Liu, Qingxue Yao, Jeremy Gillbanks, and Xin Zhao. "Research on High-Throughput Crop Root Phenotype 3D Reconstruction Using X-ray CT in 5G Era." Electronics 12, no. 2 (January 5, 2023): 276. http://dx.doi.org/10.3390/electronics12020276.

Full text

Abstract:

Currently, the three-dimensional detection of plant root structure is one of the core issues in studies on plant root phenotype. Manual measurement methods are not only cumbersome but also have poor reliability and damage the root. Among many solutions, X-ray computed tomography (X-ray CT) can help us observe the plant root structure in a three-dimensional and non-destructive form under the condition of underground soil in situ. Therefore, this paper proposes a high-throughput method and process for plant three-dimensional root phenotype and reconstruction based on X-ray CT technology. Firstly, this paper proposes a high-throughput transmission for the root phenotyping and utilizing the imaging technique to extract the root characteristics; then, the study adopts a moving cube algorithm to reconstruct the 3D (three-dimensional) root. Finally, this research simulates the proposed algorithm, and the simulation results show that the presented method in this paper works well.

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Yawei, and Yifei Chen. "Non-Destructive Measurement of Three-Dimensional Plants Based on Point Cloud." Plants 9, no. 5 (April 29, 2020): 571. http://dx.doi.org/10.3390/plants9050571.

Full text

Abstract:

In agriculture, information about the spatial distribution of plant growth is valuable for applications. Quantitative study of the characteristics of plants plays an important role in the plants’ growth and development research, and non-destructive measurement of the height of plants based on machine vision technology is one of the difficulties. We propose a methodology for three-dimensional reconstruction under growing plants by Kinect v2.0 and explored the measure growth parameters based on three-dimensional (3D) point cloud in this paper. The strategy includes three steps—firstly, preprocessing 3D point cloud data, completing the 3D plant registration through point cloud outlier filtering and surface smooth method; secondly, using the locally convex connected patches method to segment the leaves and stem from the plant model; extracting the feature boundary points from the leaf point cloud, and using the contour extraction algorithm to get the feature boundary lines; finally, calculating the length, width of the leaf by Euclidean distance, and the area of the leaf by surface integral method, measuring the height of plant using the vertical distance technology. The results show that the automatic extraction scheme of plant information is effective and the measurement accuracy meets the need of measurement standard. The established 3D plant model is the key to study the whole plant information, which reduces the inaccuracy of occlusion to the description of leaf shape and conducive to the study of the real plant growth status.

APA, Harvard, Vancouver, ISO, and other styles

44

Brocks, S., and G. Bareth. "EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 785–89. http://dx.doi.org/10.5194/isprs-archives-xli-b5-785-2016.

Full text

Abstract:

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

APA, Harvard, Vancouver, ISO, and other styles

45

Brocks, S., and G. Bareth. "EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 785–89. http://dx.doi.org/10.5194/isprsarchives-xli-b5-785-2016.

Full text

Abstract:

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

APA, Harvard, Vancouver, ISO, and other styles

46

Nielsen, M., H. J. Andersen, D. C. Slaughter, and E. Granum. "Ground truth evaluation of computer vision based 3D reconstruction of synthesized and real plant images." Precision Agriculture 8, no. 1-2 (January 13, 2007): 49–62. http://dx.doi.org/10.1007/s11119-006-9028-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Mansilla, Catalina, Maria Helena Novais, Enne Faber, Diego Martínez-Martínez, and J. Th De Hosson. "On the 3D reconstruction of diatom frustules: a novel method, applications, and limitations." Journal of Applied Phycology 28, no. 2 (July 4, 2015): 1097–110. http://dx.doi.org/10.1007/s10811-015-0653-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Tross, Michael C., Mathieu Gaillard, Mackenzie Zwiener, Chenyong Miao, Ryleigh J. Grove, Bosheng Li, Bedrich Benes, and James C. Schnable. "3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves." PeerJ 9 (December 22, 2021): e12628. http://dx.doi.org/10.7717/peerj.12628.

Full text

Abstract:

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.

APA, Harvard, Vancouver, ISO, and other styles

49

Hesse, Linnea, Katharina Bunk, Jochen Leupold, Thomas Speck, and Tom Masselter. "Structural and functional imaging of large and opaque plant specimens." Journal of Experimental Botany 70, no. 14 (April 24, 2019): 3659–78. http://dx.doi.org/10.1093/jxb/erz186.

Full text

Abstract:

AbstractThree- and four-dimensional imaging techniques are a prerequisite for spatially resolving the form–structure–function relationships in plants. However, choosing the right imaging method is a difficult and time-consuming process as the imaging principles, advantages and limitations, as well as the appropriate fields of application first need to be compared. The present study aims to provide an overview of three imaging methods that allow for imaging opaque, large and thick (>5 mm, up to several centimeters), hierarchically organized plant samples that can have complex geometries. We compare light microscopy of serial thin sections followed by 3D reconstruction (LMTS3D) as an optical imaging technique, micro-computed tomography (µ-CT) based on ionizing radiation, and magnetic resonance imaging (MRI) which uses the natural magnetic properties of a sample for image acquisition. We discuss the most important imaging principles, advantages, and limitations, and suggest fields of application for each imaging technique (LMTS, µ-CT, and MRI) with regard to static (at a given time; 3D) and dynamic (at different time points; quasi 4D) structural and functional plant imaging.

APA, Harvard, Vancouver, ISO, and other styles

50

Moualeu-Ngangué, Dany, Maria Bötzl, and Hartmut Stützel. "First Form, Then Function: 3D Reconstruction of Cucumber Plants (Cucumis sativus L.) Allows Early Detection of Stress Effects through Leaf Dimensions." Remote Sensing 14, no. 5 (February 23, 2022): 1094. http://dx.doi.org/10.3390/rs14051094.

Full text

Abstract:

Detection of morphological stress symptoms through 3D examination of plants might be a cost-efficient way to avoid yield losses and ensure product quality in agricultural and horticultural production. Although the 3D reconstruction of plants was intensively performed, the relationships between morphological and physiological plant responses to salinity stress need to be established. Therefore, cucumber plants were grown in a greenhouse in nutrient solutions under three salinity treatments: 0, 25, and 50 mM NaCl. To detect stress-induced changes in leaf transversal and longitudinal angles and dimensions, photographs were taken from plants for 3D reconstruction through photogrammetry. For assessment of physiological stress responses, invasive leaf measurements, including the determination of leaf osmotic potential, leaf relative water content, and the leaf dry to fresh weight ratio, were performed. The transversal and longitudinal leaf dimensions revealed statistically significant differences between stressed and control plants after 60 °Cd (day 3) for the leaves which appeared before stress imposition. Strong correlations were found between the transversal width and some investigated physiological traits. Morphological changes were shown as indicators of physiological responses of leaves under salinity stress.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Plant 3D reconstruction'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles