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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.

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This study aims to investigate the combination of speckle pattern analysis, polarization parameters and chemometric tools to predict the optical absorption and scattering properties of materials. For this purpose, an optical setup based on light polarization and speckle measurements was developed and turbid samples were measured at 405 nm and 660 nm. First, backscattered polarized speckle acquisition was performed on a set of 41 samples with various scattering (µ s) and absorbing (µ a) coefficients. Then, several parameters were computed from the polarized speckle images and prediction models were built using stepwise-Multiple Linear Regression. For scattering media, µ s was predicted with R² > 0.9 using two parameters. In the case of scattering and absorbing media, prediction results using two parameters were R² = 0.62 for µ s and R² = 0.8 for µ a. The overall results obtained in this research showed that the combination of speckle pattern analysis, polarization parameters and chemometric tools to predict the optical bulk properties of materials show interesting promises.

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