Implementation of hyperspectral image analysis for evaluating table grape quality on bunch and berry level

Downy mildew (Plasmopara viticola), powdery mildew (Erysiphe necator) and bunch rot (Botrytis cinerea) are the most prevalent fungal diseases in viticulture.

It is well known that high oxygen levels and high ageing temperatures are detrimental to white wine’s composition and ageing capacity. However, these results, though valuable

Aims: This study aims to: (1) characterize the agroclimatic conditions of the Portuguese Denominations of Origin, using a compound index that combines thermal and soil water balance conditions and a high-resolution climatic dataset (~1 km spatial resolution); (2) categorize the main grapevine varieties as a function of this compound index.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

Suite à des expérimentations de solarisation artificielle réalisées en 1999 en conditions réelles de culture, à partir de matériels réfléchissants partiellement colorés en vert, en bleu