Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views. The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber. An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations. The whole system, including the programmable growth chamber, robot, scanner, data transfer and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention. As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana(L.), and the monocot barley (Hordeum vulgare L.) plants under their diurnal light/dark cycles.
Research on increasing the production of crops is increasingly important these days. This research needs a way to quantitatively measure the 3D growth of plants under controlled environments to allow a cost versus benefits analysis. Plant scientists need a non-invasive, non-destructive method to quantitatively measure the 3D growth of plants. Traditional methods, for example, measuring weight, area or volume, often negatively affects the future plant growth. Also the manual nature of this measurement can be quite time consuming, tedious and error prone. Some recent effort have been reported in the literature about the construction of autonomous systems for plant phenotyping, but these are not practical for large scale accurate 3D plant growth computation. To the best of our knowledge, we are the first in the world to attempt truly 3D approach via robot assisted plant growth analysis using 3D imaging and laser scanning technology. We describe an automated system to perform 3D plant modelling using a laser scanner mounted on a robot arm to capture 3D plant data. We present a detailed overview of the system integration, including the robotic arm, laser scanner and a programmable growth chamber. We also show some results on reconstructing the 3D model of a growing plant which is better than the current state of the art.Autonomous robotic imaging systems are emerging as an important research area for different kind of bio-engineering applications. Analyzing different properties of plants is of fundamental interest in plant science research. Some of the most common applications include measuring the growth of a growing plant, tracking a specific organ over time, phenotyping (analyzing biological properties), etc. The naive methods used by biologists for measuring plant growth are often destructive in nature, preventing the growth analysis of a growing plant. Also, considering the manual effort required and the chances of measurement error are severe restrictions for real time analysis. With the advancements of robotic technologies and low cost near-infrared laser scanners, 3D automatic non-invasive analysis of growing plants is becoming possible. One motivation of this work is to introduce a system which is capable of capturing 3D plant data and which can process the captured data in real time. 1 Such a system would be beneficial for large scale phenotyping and crop monitoring in future. Usually a crop monitoring system involves growth chamber, inside which the plant is grown and monitored over time. We believe that the functionality of the chamber is an integral part of a automation system. More specifically, apart from the data capture using the robot, the system should be integrated as a whole. We present such a system below.Due to its non-invasive non-contact nature, imaging techniques are now being used extensively for analysis in various fields including medical science, biology, etc. Most applications still involve 2D image analysis and exhibits the inherent limitations of 2D analysis. A 3D laser scanner offers ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.