On-the-go resistivity sensors employment to support soil survey for precision viticulture

Dans le contexte viticole actuel, la prise de conscience que chaque cépage ne trouve son expression qualitative maximale que dans certains terroirs bien définis

Climate is a key variable for grapevine development and berry ripening processes. At mesoscale level, climate spatial variations are often determined empirically, as weather station networks are generally not dense enough to account for local climate variations.

During the more than 190 years since the founding of the first ampelographic collection, the creation of a series of collections is attested on the territory of the Republic of Moldova, each operating in different historical periods and socio-economic conditions,

Agroecology is an integrated approach that simultaneously applies ecological and social concepts and principles to redesign and manage food and agricultural systems, promoting agroecosystems with the necessary biological, socio-economic, and institutional diversity and alignment to support greater efficiency. Thus, several studies have been carried out at promoting the adoption of more agroecological practices among farmers and a wider audience concerning soil conservation and health maintenance.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).