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Rootstocks serve as a strategic tool for grapevine adaptation to specific biotic and abiotic conditions and for managing vine growth, grape yield, and berry composition in commercial vineyards. This study investigates the influences of four different rootstock varieties (101-14 MGt, 3309 C, 110 R, and 140 Ru) on the viticultural performance of 'Xinomavro' vines, a prominent Greek red winegrape varietal. By conducting a two-year field experiment using various rootstocks, we assessed parameters related to water status, vegetative growth, yield, and berry composition. Our results revealed that rootstock selection has a significant impact on vine development, especially in terms of berry size and the concentrations of secondary metabolites. Principal component analysis confirmed the complex interaction between rootstock vigor and vine productivity. This study underscores the importance of rootstock variety in manipulating grapevine characteristics, particularly for the 'Xinomavro' variety, in response to regional climatic conditions. [ABSTRACT FROM AUTHOR]

A three-year study was conducted to investigate the effects of two timings—at berry set and at veraison—of post-bloom leaf removal (LR) applications from the basal sections of the shoot on the growth, yield, and berry composition of Vitis vinifera L. cv. Xinomavro, the major red winegrape of North Greece. LR at berry set drastically reduced yield by affecting all its components while increasing the ratio of secondary foliage per total leaf area. LR at veraison had a milder effect on growth and yield. Both treatments increased sugar and phenolic compound levels in berries, while early LR increased the contribution of more stable forms of anthocyanins. Additionally, early LR increased the skin-to-pulp ratio, suggesting that it could be an effective viticultural technique for more concentrated red wines. Overall, both LR timings improved most of the berry attributes compared to the non-treated vines; however, the earlier application can be recommended when aiming at the production of deep-colored and richly flavored wines. [ABSTRACT FROM AUTHOR]

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content ( ∘ Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties—Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah—during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR–SWIR spectrum (350–2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) ( ∘ Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input–multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean R 2 values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices. [ABSTRACT FROM AUTHOR]

In recent years, the agricultural sector has turned to robotic automation to deal with the growing demand for food. Harvesting fruits and vegetables is the most labor-intensive and time-consuming among the main agricultural tasks. However, seasonal labor shortage of experienced workers results in low efficiency of harvesting, food losses, and quality deterioration. Therefore, research efforts focus on the automation of manual harvesting operations. Robotic manipulation of delicate products in unstructured environments is challenging. The development of suitable end effectors that meet manipulation requirements is necessary. To that end, this work reviews the state-of-the-art robotic end effectors for harvesting applications. Detachment methods, types of end effectors, and additional sensors are discussed. Performance measures are included to evaluate technologies and determine optimal end effectors for specific crops. Challenges and potential future trends of end effectors in agricultural robotic systems are reported. Research has shown that contact-grasping grippers for fruit holding are the most common type of end effectors. Furthermore, most research is concerned with tomato, apple, and sweet pepper harvesting applications. This work can be used as a guide for up-to-date technology for the selection of suitable end effectors for harvesting robots. [ABSTRACT FROM AUTHOR]