Non-linear unmixing as an innovative tool to detect vine diseases in UAVs, airborned and satellite images: preliminary results

Vine growth circumstances are becoming warmer and drier because of climate change. Higher temperatures advance ripening to a point in the season less conducive to the production of fine wine, while drought reduces yields (Van Leeuwen et al., 2019). Several wine-producing regions around the world have already recognized threats to their viticultural viability (Santos et al., 2020). An economical and cost-effective strategy for adaptation is the employment of late-ripening, drought-resistant plant material (varieties, clones, and rootstocks).

An experiment was carried out to test the relevance of using satellite images (NDVI) to define locations of plant monitoring systems.

The use of bentonite in oenology rounds around the limpidity and the stability that determine consumer acceptability. As a matter of fact, the haze formation in wine reduces its commercial value and makes it unacceptable for sale. Stabilization treatments are, therefore, essential to ensure a long-time limpidity and to forecast the formation of deposits in the bottle. Bentonite that is normally used in oenology for clarifying-fining purpose, shows a natural clay-based mineral structure allowing it to swell and to jelly in water and hence in must and wine.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.

The type of wine is determined by environmental, plant materials and human factors. These factors are numerous and interact together, which makes it difficult to determine the hierarchy of their effects