PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments

Abstract

BackgroundPlant science uses increasing amounts of phenotypic data to unravel the complex interactions between biological systems and their variable environments. Originally, phenotyping approaches were limited by manual, often destructive operations, causing large errors. Plant imaging emerged as a viable alternative allowing non-invasive and automated data acquisition. Several procedures based on image analysis were developed to monitor leaf growth as a major phenotyping target. However, in most proposals, a time-consuming parameterization of the analysis pipeline is required to handle variable conditions between images, particularly in the field due to unstable light and interferences with soil surface or weeds. To cope with these difficulties, we developed a low-cost, 2D imaging method, hereafter called PYM. The method is based on plant leaf ability to absorb blue light while reflecting infrared wavelengths. PYM consists of a Raspberry Pi computer equipped with an infrared camera and a blue filter and is associated with scripts that compute projected leaf area. This new method was tested on diverse species placed in contrasting conditions. Application to field conditions was evaluated on lettuces grown under photovoltaic panels. The objective was to look for possible acclimation of leaf expansion under photovoltaic panels to optimise the use of solar radiation per unit soil area.ResultsThe new PYM device proved to be efficient and accurate for screening leaf area of various species in wide ranges of environments. In the most challenging conditions that we tested, error on plant leaf area was reduced to 5% using PYM compared to 100% when using a recently published method. A high-throughput phenotyping cart, holding 6 chained PYM devices, was designed to capture up to 2000 pictures of field-grown lettuce plants in less than 2 h. Automated analysis of image stacks of individual plants over their growth cycles revealed unexpected differences in leaf expansion rate between lettuces rows depending on their position below or between the photovoltaic panels.ConclusionsThe imaging device described here has several benefits, such as affordability, low cost, reliability and flexibility for online analysis and storage. It should be easily appropriated and customized to meet the needs of various users.Electronic supplementary materialThe online version of this article (10.1186/s13007-017-0248-5) contains supplementary material, which is available to authorized users.

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

“…Given its suitability for applications in many areas, the RPi has been used in a large number of research projects, from sensor networks [ 4 , 5 , 6 ], vehicle active safety systems [ 7 ], e-health care [ 8 ], big data analytics [ 9 ], and image analysis [ 10 ], to various other areas. It has also been widely used in educational projects [ 11 ], and in the construction of affordable and energy-efficient clusters consisting of up to 300 nodes [ 12 ], which partially offsets its lack of computing power.…”

Automated Tranquilizer

“…The Raspberry Pi 3 is the third generation of a microcontroller [12]. It is more efficient than a microcontroller.…”

“…Updation of the data read from sensors is also done by using this module. It also provides an efficient way to add additional features to the system in future and also enhancements with the existing system is also feasible [12].…”

Measuring Surface Area of Leaf based on Multi-Angle Images

“…The other is LA estimation from pixel statistics method, such as portable scanning planimeter, handheld device, image processing and so on. These devices use optical scanning to obtain the leaf image, and use proper software to statistic the total pixel of the leaf to calculate LA [7][8][9]. However, since leaves are generally not flat and area measurement of non-flat leaves will cause a certain degree of damage to leaf tissue, they should be flattened before two dimensional plane measurements are carried out.…”