Hyperspectral imaging and machine learning for monitoring grapevine physiology

[lwp_divi_breadcrumbs home_text="IVES" use_before_icon="on" before_icon="||divi||400" module_id="publication-ariane" _builder_version="4.19.4" _module_preset="default" module_text_align="center" text_orientation="center" custom_margin="65px||18px||false|false"...

The grapevine is facing significant challenges due to climate change, as rising temperatures impact its physiological traits and disrupt plant phenology.

Terroirs represent a heritage that must be studied and managed with appropriate methods; recourse to agronomic and oenological sciences alone is necessary, but is in no way sufficient without the contribution of the humanities.

The need to rationalize agricultural inputs has recently increased interest in assessing vineyard variability in order to implement variable rate input applications, so-called ‘precision viticulture’. In many viticultural areas globally, precision viticulture is already widely used such as for selective harvesting and variable rate application (VRA) of inputs such as irrigation and/or fertilizer. Robust VRA relies on having a geostatistically accurate map (of one or more vineyard attributes) requiring high sampling densities, which can be cost- and time-prohibitive to obtain. Previous work on spatial interpolation using kriging have upscaled ground-based measurements, but such upscaling strategies are applicable only when vineyard conditions are spatially continuous and satisfies the assumption of second-order stationary processes. Alternatively, mixed models that combine kriging and auxiliary information, such as the regression kriging (RK) method, are more instructive for spatial predictions. In order to improve prediction accuracies, it is therefore necessary to incorporate additional information to achieve accurate spatial patterns with low error.

Yield estimation is very important for the wine industry since provides useful information for vineyard and winery management. The early yield estimation of the grapevine provides information to winegrowers in making management decisions to achieve a better quantity and quality of grapes. In general, yield forecasts are based on destructive sampling of bunches and manual counting of berries per bunch and bunches per vine.