Computer Vision Problems in 3D Plant Phenotyping

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis. First, we present a novel system for automated and non-invasive/non-contact plant growth measurement. We exploit the recent advancements of sophisticated robotic technologies and near infrared laser scanners to build a 3D imaging system and use state-of-the-art Computer Vision algorithms to fully automate growth measurement. We have set up a gantry robot system having 7 degrees of freedom hanging from the roof of a growth chamber. The payload is a range scanner, which can measure dense depth maps (raw 3D coordinate points in mm) on the surface of an object (the plant). The scanner can be moved around the plant to scan from different viewpoints by programming the robot with a specific trajectory. The sequence of overlapping images can be aligned to obtain a full 3D structure of the plant in raw point cloud format, which can be triangulated to obtain a smooth surface (triangular mesh), enclosing the original plant. We show the capability of the system to capture the well known diurnal pattern of plant growth computed from the surface area and volume of the plant meshes for a number of plant species. Second, we propose a technique to detect branch junctions in plant point cloud data. We demonstrate that using these junctions as feature points, the correspondence estimation can be formulated as a subgraph matching problem, and better matching results than state-of-the-art can be achieved. Also, this idea removes the requirement of a priori knowledge about rotational angles between adjacent scanning viewpoints imposed by the original registration algorithm for complex plant data. Before, this angle information had to be approximately known. Third, we present an algorithm to classify partially occluded leaves by their contours. In general, partial contour matching is a NP-hard problem. We propose a suboptimal matching solution and show that our method outperforms state-of-the-art on 3 public leaf datasets. We anticipate using this algorithm to track growing segmented leaves in our plant range data, even when a leaf becomes partially occluded by other plant matter over time. Finally, we perform some experiments to demonstrate the capability and limitations of the system and highlight the future research directions for Computer Vision based plant phenotyping

Magnetic Field resulting from non-linear electrical transport in single crystals of charge-ordered Pr0.63_{0.63} Ca0.37_{0.37} MnO3_{3}}

In this letter we report that the current induced destabilization of the charge ordered (CO) state in a rare-earth manganite gives rise to regions with ferromagnetic correlation. We did this experiment by measurement of the I-V curves in single crystal of the CO system Pr$_{0.63}$Ca$_{0.37}$MnO$_{3}$ and simultanously measuring the magnetization of the current carrying conductor using a high T$_c$ SQUID working at T = 77K. We have found that the current induced destabilization of the CO state leads to a regime of negative differential resistance which leads to a small enhancement of the magnetization of the sample, indicating ferromagnetically aligned moments.Comment: 4 pages LateX, 4 eps figure

Geometry Reconstruction of Plants

Synthetic modelling and reconstruction of the geometry of real plants have been a center of attention of research for decades. Due to the complex architecture and growth pattern of plants, accurate modelling of the plant geometry is an extremely challenging task. Although realistic modelling of plants are widely studied in the context of computer graphics research, it also has profound impact on the biological study of plants. In order to perform various types of simulation studies under different environmental conditions and in understanding the physiology of plants in more details, synthetic models can be an extremely useful tool. Synthetic modelling approaches can be broadly categorized into three types. The first type is the rule based procedural modeling approach, which does not account the real data into consideration. The second type of approach (also known as data driven modelling) performs modelling based on the real data obtained from 3D acquisition procedures. The third type of approach is interactive, which is based on user assistance. In this chapter, we focus on the modelling of the second category and revisit the recent state-of-the-art techniques performing reconstruction of plant geometry from real data. The algorithms can be classified into different interlinked categories, which constitute the general pipeline of geometry reconstruction in data driven modelling framework. In the context of biological relevance of different types of techniques, we discuss about the strengths and limitations of the approaches and the need of prior botanical knowledge to reconstruct the plant geometry in biologically feasible manner. Finally, we explore the quantitative assessment techniques which can be used to measure the quality of the reconstruction result with respect to the actual data

Skeletonization of Plant Point Cloud Data Using Stochastic Optimization Framework

International audienceSkeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of beta-splines to form acurve treedefined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move toward the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art

Skeletonization of Plant Point Cloud Data Using Stochastic Optimization Framework

Skeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move toward the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art

3D Plant Phenotyping: All You Need is Labelled Point Cloud Data

International audienceIn the realm of modern digital phenotyping technologicaladvancements, the demand of annotated datasets is increasing for eithertraining machine learning algorithms or evaluating 3D phenotypingsystems. While a few 2D datasets have been proposed in the communityin last few years, very little attention has been paid to the constructionof annotated 3D point cloud datasets. There are several challengesassociated with the creation of such annotated datasets. Acquiringthe data requires instruments having good precision and accuracylevels. Reconstruction of full 3D model from multiple views is a challengingtask considering plant architecture complexity and plasticity, aswell as occlusion and missing data problems. In addition, manual annotationof the data is a cumbersome task that cannot easily be automated.In this context, the design of synthetic datasets can play animportant role. In this paper, we propose an idea of automatic generationof synthetic point cloud data using virtual plant models. Our approachleverages the strength of the classical procedural approach (likeL-systems) to generate the virtual models of plants, and then performpoint sampling on the surface of the models. By applying stochasticityin the procedural model, we are able to generate large number of diverseplant models and the corresponding point cloud data in a fully automaticmanner. The goal of this paper is to present a general strategyto generate annotated 3D point cloud datasets from virtual models. Thecode (along with some generated point cloud models) are available at:https://gitlab.inria.fr/mosaic/publications/lpy2pc