Artificial intelligence (AI)-based protein modeling for the interpretation of grapevine genetic variants

Australia is the seventh largest producer of wine and crushed 1.42 million tonnes of wine grapes in the 2001 vintage.

Knowledge on the reflectance spectrum of soil is potentially useful since it carries information on soil chemical composition that can be used to the planning of agricultural practices. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on soils from two vineyards in south Brazil. The vineyards are close to each other, are on different geological formations, but were subjected to the same management. The objective was to detect spectral differences between the two areas, correlating these differences to variations in their chemical composition, to assess the technique’s potential to predict soil attributes from reflectance data.To that end, soil samples were collected from ten selected vine parcels. Chemical analysis yield data on concentration of twenty-one soil attributes, and spectroradiometry was performed on samples. Chemical differences significant to a 95% confidence level between the two studied areas were found for six soil attributes, and the average reflectance spectra were separated by this same level along most of the observed spectral domain. Correlations between soil reflectance and concentrations of soil attributes were looked for, and for ten soil traits it was possible to define wavelength domains were reflectance and concentrations are correlated to confidence levels from 95% to 99%. Partial Least Squares Regression (PLSR) analyses were performed comparing measured and predicted concentrations, and for fifteen out of 21 soil traits we found Pearson correlation coefficients r > 0.8. These preliminary results, which have to be validated, suggest that variations of concentration in the investigated soil attributes induce differences in reflectance that can be detected by spectroradiometry. Applications of these observations include the assessment of the chemical content of soils by spectroradiometry as a fast, low-cost alternative to chemical analytical methods.

Context and purpose of the study. Current remote sensing strategies rely heavily on reflectance data and energy balance modelling using thermal imagery to estimate crop water use and stress.

This work aims at identifying the most appropriate marketing strategies to inform consumers of the global market about the added value of the wines of Friuli Venezia Giulia.

Introduction: Malolactic fermentation (MLF) is a common process in winemaking to reduce wine acidity, maintain microbial stability and modify wine aroma. However, successful MLF is often hampered by their sluggish or stuck activity of malolactic bacteria (MLB) which may be caused by nutrient deficiency, especially when MLB are inoculated after alcoholic fermentation (Alexandre et al., 2004; Lerm et al., 2010). Identification and characterization of essential nutrients and growth factors for MLB allows for production of highly efficient nutrient supplements for MLF.