Leaf wetness duration modeling for the improvement of fungal risk evaluation in low tech greenhouse

Airborne fungal diseases are a serious concern in the floricultural sector for their severe effects on plant aesthetical appearance, and their control represents a crucial aspect for ornamental plant marketability. The fungal pathogen growth is strongly related to the micrometeorological conditions within the plant canopy, particularly to air temperature, air relative humidity, and leaf wetness, used by the epidemiological models and alert systems to support Integrated Pest Management practices. While air temperature and relative humidity can be easily monitored by quite cheap and reliable sensors, leaf wetness measure is more difficult to achieve, more expensive, and often less accurate. However, this parameter is crucial for the operational use of many epidemiological models, such as those of grey mould, a globally diffuse pathogen responsible for important economic losses in the horticultural sector. In this work, three different procedures, i.e., a leaf wetness sensor, a dew point depression (DPD) method, and the use of a constant air relative humidity threshold (87%) were used to estimate the local leaf wetness duration with a spatial resolution of 2 m². The uncertainty range of the DPD method was adjusted using the LW sensor output to feed an adapted grey mould development model for the determination of the related disease risk in a low-tech greenhouse with rose plants used for the cut flower production. Preliminary results suggest the possibility to use an integrated system (leaf wetness sensor + DPD model) to improve the actual discrimination capability and to guide the fungicide treatments for rose pest control with a high spatial resolution.