Assessing opportunities for selective winery vintage with a market-driven composite index

An opportunity index (OISV) is proposed for selective vine harvest management to ensure vineyard sustainability making use of precision farming technologies. Vigour maps derived from remote sensors are the basis of the method. In terms of validation, the index was applied in 36 vineyard fields of different varieties in Raimat (Lleida, northeast Spain). The OISV is based on three components: (i) the spatial variability in vine vigour (to ensure variability in the quality of grapes), (ii) the spatial structure or pattern of vigour (to facilitate harvesting operations), and (iii) the availability of a minimum productive quality area within the plot (to ensure that benefits derived from the differentiation of the final product will compensate for the expenses of differential management). The results suggest that only few plots were suitable for selective vintage, although an acceptable agreement was obtained when comparing the plots harvested selectively by the winery and those classified as favourable by the OISV. The method is reliable and also allows varying the parameter specifications according to the logistics of each winery and/or actual market conditions. However, currently, the OISV can only be applied at plot level and future versions should address application at the whole vineyard scale.This study was possible thanks to the collaboration between the University of Lleida and Codorníu Winery in Raimat (Lleida, Spain

Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples

Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.This work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017, SGR 646 and 2021 LLAV 00088), by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / ERDF (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project]) and by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / European Union NextGeneration / PRTR (grantTED2021-131871B-I00 [DIGIFRUIT project]). We would also like to thank the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and the European Social Fund (ESF) for financing Juan Carlos Miranda’s pre-doctoral fellowship (2020 FI_B 00586). The work of Jordi Gené-Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU.info:eu-repo/semantics/publishedVersio