RESUMO
BACKGROUND: 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. RESULTS: The 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. CONCLUSIONS: The 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.
RESUMO
Wood rot fungi form one of the main classes of phytopathogenic fungus. The group includes many species, but has remained poorly studied. Many species belonging to the Ganoderma genus are well known for causing decay in a wide range of tree species around the world. Ganoderma boninense, causal agent of oil palm basal stem rot, is responsible for considerable yield losses in Southeast Asian oil palm plantations. In a large-scale sampling operation, 357 sporophores were collected from oil palm plantations spread over peninsular Malaysia and Sumatra and genotyped using 11 SSR markers. The genotyping of these samples made it possible to investigate the population structure and demographic history of G. boninense across the oldest known area of interaction between oil palm and G. boninense. Results show that G. boninense possesses a high degree of genetic diversity and no detectable genetic structure at the scale of Sumatra and peninsular Malaysia. The fact that few duplicate genotypes were found in several studies including this one supports the hypothesis of spore dispersal in the spread of G. boninense. Meanwhile, spatial autocorrelation analysis shows that G. boninense is able to disperse across both short and long distances. These results bring new insight into mechanisms by which G. boninense spreads in oil palm plantations. Finally, the use of approximate Bayesian computation (ABC) modelling indicates that G. boninense has undergone a demographic expansion in the past, probably before the oil palm was introduced into Southeast Asia.