TY - JOUR AU - Valle, Benoît AU - Simonneau, Thierry AU - Boulord, Romain AU - Sourd, Francis AU - Frisson, Thibault AU - Ryckewaert, Maxime AU - Hamard, Philippe AU - Brichet, Nicolas AU - Dauzat, Myriam AU - Christophe, Angélique PY - 2017 DA - 2017/11/08 TI - PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments JO - Plant Methods SP - 98 VL - 13 IS - 1 AB - Plant 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. SN - 1746-4811 UR - https://doi.org/10.1186/s13007-017-0248-5 DO - 10.1186/s13007-017-0248-5 ID - Valle2017 ER -