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"Viticulture"
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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.
Journal Article
Wine economy in Byzantine Shivta
Viticulture was a vital agricultural and economic activity during the Byzantine period, also in marginal regions like the Negev Desert. Innovative dryland farming techniques, such as runoff harvesting systems, terraces, and pigeon towers, enabled intensive grape cultivation and a thriving wine export economy. This study focuses on the resilience and adaptability of viticulture in the hinterland of Shivta, analyzing how climatic challenges like aridification and drought tested Byzantine water management strategies. The AGENTS model, developed in NetLogo, integrates various components to simulate viticulture dynamics in the Zetan watershed, calculating water availability, crop yields, and labor costs. The results show that higher runoff ratios improve yield efficiency, while excessive runoff ratios diminish productivity. Prolonged droughts significantly decrease wine production and extend recovery times beyond a decade. Wetter climatic scenarios slightly enhance yield efficiency but do not overcome structural limitations, highlighting the fragile nature of viticulture in the Negev desert. Overall, this study highlights the importance of effective water management in sustaining agriculture and the constraints that limited resilience in Shivta's agricultural system. The modeling approach offers insights applicable to other regions and historical contexts facing environmental challenges.
Journal Article
Future Climate Change Impacts on European Viticulture: A Review on Recent Scientific Advances
Climate change is a continuous spatiotemporal reality, possibly endangering the viability of the grapevine (Vitis vinifera L.) in the future. Europe emerges as an especially responsive area where the grapevine is largely recognised as one of the most important crops, playing a key environmental and socio-economic role. The mounting evidence on significant impacts of climate change on viticulture urges the scientific community in investigating the potential evolution of these impacts in the upcoming decades. In this review work, a first attempt for the compilation of selected scientific research on this subject, during a relatively recent time frame (2010–2020), is implemented. For this purpose, a thorough investigation through multiple search queries was conducted and further screened by focusing exclusively on the predicted productivity parameters (phenology timing, product quality and yield) and cultivation area alteration. Main findings on the potential impacts of future climate change are described as changes in grapevine phenological timing, alterations in grape and wine composition, heterogeneous effects on grapevine yield, the expansion into areas that were previously unsuitable for grapevine cultivation and significant geographical displacements in traditional growing areas. These compiled findings may facilitate and delineate the implementation of effective adaptation and mitigation strategies, ultimately potentiating the future sustainability of European viticulture.
Journal Article
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.
Journal Article
A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications
This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus database. The analysis shows that the researchers from the United States, China, Italy and Spain lead the precision agriculture through UAV applications. In terms of employing UAVs in PV, researchers from Italy are fast extending their work followed by Spain and finally the United States. Additionally, the paper provides a comprehensive study on popular journals for academicians to submit their work, accessible funding organizations, popular nations, institutions, and authors conducting research on utilizing UAVs for precision agriculture. Finally, this study emphasizes the necessity of using UAVs in PV as well as future possibilities.
Journal Article
Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery
Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed.
Journal Article