International audiencePlant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection

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Satellite multi-sensor data were used to investigate the evolution in time and space of Lake Trasimeno, a shallow and turbid lake in central Italy. Large-swath MERIS and MODIS sensors were proposed for regular broad scale monitoring of water quality, having compared the retrieved chlorophyll-a (Chl-a) concentration, Secchi disk (SD) depth and surface water temperature with the 2005-2008 time-series of the in situ data. Although, in a shorter time span, also the MERIS-derived total suspended matter (TSM) matched the in situ data. MERIS-derived water quality products confirmed the meso-eutrophic conditions of Lake Trasimeno (average Chl-a = 8.5 mg/m 3 ) and the low levels of transparency (average SD = 1 m). A negative correlation found between water levels and Chl-a suggest the importance of maintaining water levels as close as possible to the hydrometric zero. A spatial analysis of TSM also reveals how small tributaries may affect the load of suspended solids in the southern part of the lake. Higher spatial resolution satellite images were exploited both to describe land use/cover transformation from 1978 to 2008 and to assess the recent changes in macrophyte colonisation patterns. Land cover change detection analysis results showed a decrease in cultivated areas starting from the early Nineties and the subsequent increase in unproductive terrain (bare land and pastures) and natural woods as well as the changing fragmentation of agricultural areas through time. A reduction in macrophyte beds from 2003 to 2008 was also observed. We expect the results of this study to support local water authorities in redrawing the management plan of Lake Trasimeno.

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