Analysis of the scientifc knowledge structure on automation in the wine industry: a bibliometric and systematic review

The objective of this research is to analyze the knowledge structure of the academic literature indexed in the Core Collection of the Web of Science on automation in the wine industry, from the frst registered article in 1996 to 2022, in order to identify the latest trends in the study of this subject. A bibliometric and systematic analysis of the literature was carried out. First, for the quantitative analysis of the scientifc production, the bibliometric study was conducted, using the WoS database for data collection and the VosViewer and Bibliometrix applications to create the network maps. Second, once the literature had been examined quantitatively, content analysis was undertaken using the PRISMA methodology. The results show, among other aspects, the uneven distribution of the examined scientifc production from 1996 to 2022, that computer vision, data aggregation, life cycle assessment, precision viticulture, extreme learning machine and collaborative platforms are the major current keywords and the predominance of Spain and Italy in terms of scientifc production in the feld. There are various justifcations which support the originality of this study. First, it contributes to the nderstanding of academic literature and the identifcation of the most recent trends in the study of automation in the wine industry. Second, to the best of our knowledge, no prior bibliometric studies have considered this topic. Third, this research evaluates the literature from the frst record to the year 2022, thereby providing a comprehensive analysis of the scientifc production.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

The Use of Computer Vision to Combat Losses from Disease in Grapevines

The use of computer vision to support and automate agriculture and viticulture is increasing. Therefore, it is important to continuously test new technologies and equipment. Management of pests and diseases in viticulture is a labour-intensive task. This study aims to investigate current technologies in computer vision that could be applied to disease and pest detection in viticulture and the application of transfer learning on segmentation networks. This study also implements a case study and applies computer vision for disease and pest detection. Observation of limitations in the network's performance on testing images, after training on the limited data set, suggests that careful control is needed over lighting conditions in the image capture environment. Although initial results are positive, a larger training dataset is recommended to achieve a greater level of accuracy. Keywords: Artificial Intelligence, Computer Visions, Viticulture, Sensors DOI: 10.7176/CEIS/14-3-01 Publication date:August 31st 202

Vineyard yield estimation using image analysis – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation is one of the main goals of the wine industry, this because with an accurate yield estimation it is possible to have a significant reduction in production costs and a better management of the wine industry. Traditional methods for yield estimation are laborious and time consuming, for these reasons in the last years we are witnessing to the development of new methodologies, most of which are based on image analysis. Thanks to the continuous updating and improvement of the computer vision techniques and of the robotic platforms, image analysis applied to the yield estimation is becoming more and more efficient. In fact the results shown by the different studies are very satisfying, at least as regards the estimation of what is possible to see, while are under development several procedures which have the objective to estimate what is not possible to see, due to bunch occlusion by leaves and by others clusters. I this work the different methodologies and the different approaches used for yield estimation are described, including both traditional methods and new approaches based on image analysis, in order to present the advantages and disadvantages of each of themN/

Grapevine yield prediction using image analysis - improving the estimation of non-visible bunches

Yield forecast is an issue of utmost importance for the entire grape and wine sectors. There are several methods for vineyard yield estimation. The ones based on estimating yield components are the most commonly used in commercial vineyards. Those methods are generally destructive and very labor intensive and can provide inaccurate results as they are based on the assessment of a small sample of bunches. Recently, several attempts have been made to apply image analysis technologies for bunch and/or berries recognition in digital images. Nonetheless, the effectiveness of image analysis in predicting yield is strongly dependent of grape bunch visibility, which is dependent on canopy density at fruiting zone and on bunch number, density and dimensions. In this work data on bunch occlusion obtained in a field experiment is presented. This work is set-up in the frame of a research project aimed at the development of an unmanned ground vehicle to scout vineyards for non-intrusive estimation of canopy features and grape yield. The objective is to evaluate the use of explanatory variables to estimate the fraction of non-visible bunches (bunches occluded by leaves). In the future, this estimation can potentially improve the accuracy of a computer vision algorithm used by the robot to estimate total yield. In two vineyard plots with Encruzado (white) and Syrah (red) varieties, several canopy segments of 1 meter length were photographed with a RGB camera and a blue background, close to harvest date. Out of these images, canopy gaps (porosity) and bunches’ region of interest (ROI) files were computed in order to estimate the corresponding projected area. Vines were then defoliated at fruiting zone, in two steps and new images were obtained before each step. Overall the area of bunches occluded by leaves achieved mean values between 67% and 73%, with Syrah presenting the larger variation. A polynomial regression was fitted between canopy porosity (independent variable) and percentage of bunches not occluded by leaves which showed significant R2 values of 0.83 and 0.82 for the Encruzado and Syrah varieties, respectively. Our results show that the fraction of non-visible bunches can be estimated indirectly using canopy porosity as explanatory variable, a trait that can be automatically obtained in the future using a laser range finder deployed on the mobile platforminfo:eu-repo/semantics/publishedVersio

Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture

L'abstract è presente nell'allegato / the abstract is in the attachmen

Grapevine yield estimation using image analysis for the variety Arinto

Mestrado em Engenharia de Viticultura e Enologia (Double Degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoYield estimation can lead to difficulties in the vineyard and winery, if it is done inaccurately following wrong procedures, doing a non-representative sampling or for the human error. Moreover, the traditional yield estimation methods are time consuming and destructive because they need someone that goes into the vineyard to count the yield components and that take out from the vineyard inflorescence or bunches to count and weight the flowers and the berries. To avoid these problems and the errors that can occur on this way, the development and application of new and innovative techniques to estimate the yield through the analysis of RGB images taken under field conditions are under study from different groups of research. In our research work we’ve studied the application of counting the yield components in the images throughout all the growing season. Furthermore, we’ve studied two different algorithms that starting from the survey of canopy porosity and/or visible bunches area, can help to do an estimation of the yield. The most promising yield estimation, based on the counting of the yield components done through image analysis, was found to be at the phenological stage of four leaves out, which shown a mean absolute percent error (MA%E) of 32 ± 2% and an correlaion coefficient (r Obs,Est) between observed and estimated shoots of 0.62. The two algorithms used different models: for estimating the area of the bunches covered by leaves and to estimate the weight of the bunches per linear canopy meter. When the area of the bunches without leaf occlusion was estimated, an average percentage of occlusion generated by the bunches on the other bunches of 8%, 6% and 12% respectively at pea size, veraison and maturation, was used to estimate the total area of the bunches. When the total area of the bunches per linear canopy meter was estimated the two models to estimate the grape weight were used. Finally, to estimate the weight at harvest, the growth factors of 6.6 and 1.7 respectively, at pea size and veraison were used. The first algorithm shown a MA%E, between the estimated and observed values of yield, of - 33.59%, -9.24% and -11.25%, instead the second algorithm shown a MA%E of -6.81%, -1.35% and 0.01% respectively at pea-size, veraison and maturationN/