Clickblue text
Subscribe for free,and receive such great information every day
Plant imaging enables non-destructive automated data collection. A brief introduction to plant phenotyping information is as follows.
Reminder: The early bird registration date for the 2nd Asia-Pacific Plant Phenotyping International Conference is December 31.
With the vigorous development of science and technology, plant scientists have utilized an increasing amount of phenotypic data to study the complex interactions between biological systems and their variable environments. Initially, plant phenotyping was performed through manual measurements, often requiring destructive sampling, and the data obtained had significant human error. To address this, researchers developed a feasible alternative method—plant imaging, enabling non-destructive automated data collection. Several image analysis-based programs were developed to calculate leaf growth as the primary target for phenotypic analysis. However, most phenotypic analysis programs require time-consuming parameterization of the analysis process to cope with background changes between images, especially in the field, where unstable lighting and soil surfaces or weeds can interfere with analysis. To solve this issue, researchers developed a low-cost two-dimensional imaging method, referred to as PYM. The principle of PYM is based on the ability of plant leaves to reflect infrared wavelengths while absorbing blue light. PYM consists of a Raspberry Pi equipped with an infrared camera and a blue filter, and it includes an analysis program for calculating projected leaf area. This article tests this new method with different species under various environmental conditions, specifically measuring leaf area in lettuce grown under photovoltaic panels. The goal is to find the adaptability of leaf growth under photovoltaic panels to optimize soil use of solar radiation per unit area.
Details of the image analysis process used in the PYM program
Performance of the PYM segmentation method based on comparative background conditions
Discussion on the PYM segmentation process for different species under various environments
Research has found that the new PYM device can effectively and accurately measure the leaf area of various plants in diverse environments. Even in the most challenging conditions, the leaf area measurement error using PYM is reduced to only 5%, whereas the accuracy of recently published methods is 100%. Using a field high-throughput phenotyping cart equipped with six chain PYM devices, 2000 images of field-grown lettuce can be obtained in less than two hours. Moreover, by automatically analyzing the image library of a single plant throughout its growth cycle, significant differences in lettuce leaf growth rates can be revealed at different locations under photovoltaic panels.
Growth of lettuce cultivated in field environments
The results demonstrate that the imaging device described in this article has several advantages: low cost, flexible mobility, and the ability to analyze and store data in real-time. It can also be customized into special versions according to different research needs.
For more details, please click on the bottom “Read the original“.
Source:
Valle B, Simonneau T, et al., PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments, 2017, Plant Methods, https://doi.org/10.1186/s13007-017-0248-5
Further Reading:
The 2nd Asia-Pacific Plant Phenotyping International Conference will be held in March 2018
