Medium-resolution multispectral data from sentinel-2 to assess the damage and the recovery time of late frost on Vineyards

In a climate-change context, the advancement of phenological stages may endanger viticultural areas in the event of a late frost. This study evaluated the potential of satellite-based remote sensing to assess the damage and the recovery time after a late frost event in 2017 in northern Italian vineyards. Several vegetation indices (VIs) normalized on a two-year dataset (2018-2019) were compared over a frost-affected area (F) and a control area (NF) using unpaired two-sample t-test. Furthermore, the must quality data (total acidity, sugar content and pH) of F and NF were analyzed. The VIs most sensitive in the detection of frost damage were Chlorophyll Absorption Ratio Index (CARI), Enhanced Vegetation Index (EVI), and Modified Triangular Vegetation Index 1 (MTVI1) (-5.26%,-16.59%, and-5.77% compared to NF, respectively). The spectral bands Near-Infrared (NIR) and Red Edge 7 were able to identify the frost damage (-16.55 and-16.67% compared to NF, respectively). Moreover, CARI, EVI, MTVI1, NIR, Red Edge 7, the Normalized Difference Vegetation Index (NDVI) and the Modified Simple Ratio (MSR) provided precise information on the full recovery time (+17.7%, +22.42%, +29.67%, +5.89%, +5.91%, +16.48%, and +8.73% compared to NF, respectively) approximately 40 days after the frost event. The must analysis showed that total acidity was higher (+5.98%), and pH was lower (-2.47%) in F compared to NF. These results suggest that medium-resolution multispectral data from Sentinel-2 constellation may represent a cost-effective tool for frost damage assessment and recovery management

wGrapeUNIPD-DL: An open dataset for white grape bunch detection

National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset

Monitorare il meteo per raccogliere uve di qualit\ue0

Chardonnay, Merlot e Pinot grigio necessitano di un mese di giugno fresco e umido per l\u2019accumulo zuccherino. Per gli acidi organici, invece, sono fondamentali le temperature del mese di maggio, che hanno con le variet\ue0 oggetto di studio correlazioni sia positive sia negativ

Analisi delle scelte d'impianto in vigneti del Nordest italiano nell'attuale scenario di cambiamento climatico

Tra i fattori che influenzano la produzione di uve di qualit\ue0, il clima \ue8 il pi\uf9 rilevante. L\u2019adozione delle pi\uf9 idonee scelte tecniche e d\u2019impianto pu\uf2 permettere di sfruttare i fattori climatici favorevoli e limitare l\u2019effetto di quelli negativi, dato anche l\u2019aumento di eventi estremi legati ai cambiamenti climatici. Visti i recenti successi commerciali, le aree vitate nel Nordest italiano hanno subito una forte espansione che ha talvolta messo in secondo piano l\u2019importanza di razionali scelte d\u2019impianto. Obiettivo del lavoro \ue8 stato verificare come le scelte d\u2019impianto, combinate alle condizioni climatiche, possono influenzare la produzione

Challenges and tendencies of automatic milking systems (AMS): A 20-years systematic review of literature and patents

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000\u20132019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing inter-est in animal welfare, this review highlighted that further progress is needed to meet the consumers\u2019 demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. There-fore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research

App, dss, sensori: una panoramica sulle novità

L’intelligenza artificiale è divenuta un riferimento chiave per la gestione sostenibile delle operazioni di campo e di cantina, consentendo un risparmio di tempo e costi; tuttavia non va considerata come sostituto all’intelligenza umana ma come elemento di supporto al fine di orientare scelte più consapevol

Vite: immagini satellitari a supporto dei danni da gelo

Sempre più spesso i viticoltori si trovano ad affrontare i danni da gelate. Monitorare i danni effettivi alle gemme non è semplice e spesso il rilievo richiede molto tempo. Le immagini multispettrali possono aiutare alle rilevazioni in modo non invasivo e in breve temp

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE

Analysis and impact of recent climate trends on grape composition in north-east of Italy

Climate is the most relevant factor influencing the ripening of high quality grapes to produce a given wine style. This notion should be taken into account, given the increase of extreme weather events (EWE) related to climate change. Under this evolving climate scenario, North-East Italian wine regions have seen a recent expansion, potentially disregarding optimal planting choices. The use of marginal land, indeed, could lead to the establishment of vineyards in areas where it is not possible to take advantage of the best row orientation, slope and aspect. Under these conditions, the consequences of some EWE may be more severe. The objective of this study is to verify whether planting options in combination with climate conditions, may affect yield and fruit quality. An area localised in Northern Italy was analysed for row orientation and slope, taking advantage of QGIS tools. The area was also examined for climate conditions, using weather conditions and climate indices. Such variables were combined with 10-year yield and must composition of four varieties (Chardonnay, Pinot Gris, Merlot and Glera) by using linear regression. The paper reports the most significant relationships between climatic conditions and grapevine composition. The results showed high positive correlation between sugar concentration and the number of frost days during the year in three varieties. The sugar content was positively correlated with the relative humidity in June in three varieties and negatively correlated with the number of days with a temperature >25°C during the month of June in two varieties. The content of tartaric acid showed high correlations with thermal indices of May in all varieties