Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods

This paper presents an extended comparison study between 16 different linear and non-linear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the Savitzky−Golay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters.

Remote Sensing Vegetation Indices in Viticulture: A Critical Review

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture

Precision viticulture (PV) aims to optimize vineyard management, reducing the use of resources, the environmental impact and maximizing the yield and quality of the production. New technologies as UAVs, satellites, proximal sensors and variable rate machines (VRT) are being developed and used more and more frequently in recent years thanks also to informatics systems able to read, analyze and process a huge number of data in order to give the winegrowers a decision support system (DSS) for making better decisions at the right place and time. This review presents a brief state of the art of precision viticulture technologies, focusing on monitoring tools, i.e., remote/proximal sensing, variable rate machines, robotics, DSS and the wireless sensor network.

Relationships Between A and B/A2 Horizons of Three Soils in the Context of Viticulture

The purpose of this study was a comparison of soil parameters among superficial and sub-superficial horizons of three representative type of soils in Vale dos Vinhedos, Brazil, aiming Viticulture in general, but Precision Viticulture (PV) management as specific focus. Basic aspects of Viticulture or PV are still discussed, by instance, sampling procedures, influence of pedology on quality of wine or methods for defining the management zones. Samples were collected according to each horizon, superficial (0-20 cm in depth) or sub-superficial (>40 cm in depth), that is, A (all soils), A2 (Neossolo), Bi (Cambissolo), or Bt (Argissolo). Micro-vinification was performed for grapes derived from five classes of soil. The pedological parameters analyzed were granulometric fractions, chemical parameters and degree of flocculation. Data were organized according to a Geographic Information System (GIS) by ten classes of soil. Basic statistical analysis, line graphs, XY plots and factor analysis were used to interpret the physicochemical variables related to horizons, soil and wine. Relative Accumulation Indexes were evaluated for horizons of soil, classes of soil and wine. Data organized by horizon and type of soil showed great dispersion, so outliers were discarded and data organized by class of soil. Correlation of data related to soil horizons, A against A2/B, was evident for macronutrients. Micro-vinification altered content of K and P in wine. Relative Accumulation Indexes of soil horizons correlated to declivity, when declivity lesser than 20% occurred. Fe and Zn showed correlation among sub-superficial horizon and wine for some classes of soil.