Virtual vineyard for grapevine management purposes: A RFID/GPS application

Luvisi, Andrea; Pagano, Mario; Bandinelli, Romeo; Rinaldelli, E.; Gini, Barbara; Scartòn, Mario; Manzoni, G.; Triolo, Enrico

doi:10.1016/j.compag.2010.12.013

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…Visualisations in viticulture enable a better vineyard monitoring, reducing costs and at the same time, generating a more transparent representation of the existent variability in the vineyard, which is valuable for the optimisation • AgroDSS [31] • AquaGIS [32] • • ATLAS [33] • • Blauth et al [34] • Byishimo et al [35] • CAMDT [36] • CropGIS [37] • • CropSAT [38] • • DIDAS [39] • DyNoFlo [40] • • Galindo et al [41] • GeoVisage [42] • Geovit [43] • GramyaVikas [44] • HydroQual [45] • Li et al [46] • LMTool [47] • • Luvisi et al [48] • mDSS [49] • SmartScape [50] • • VBoxReporting [51] Vite.net [52] • • • visualizeR [8] • ViPER [53] • Ochola et al [54] • Falcao et al [55] • LandCaRe DSS [56] • ValorE [57] • Agroland [58] • Gandhi et al [59] • • CaNaSTA [60] • eFarmer [61] • FARMERS [62] • PlanteInfo [63] • CropScape [64] • SIMAGRI [65] • FDSSFIS [66] • MOTIFS [67] • CarrotAge [68] • AgriSensor [69] • • • CognitiveInputs [70] • • Ruß et al [71] Tan et al…”

Section: Viticulturementioning

confidence: 99%

A review of visualisations in agricultural decision support systems: An HCI perspective

Gutiérrez

Htun

Schlenz³

et al. 2019

Computers and Electronics in Agriculture

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Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to a variety of stakeholders, including farmers, advisers, researchers and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems the problem gets even more challenging. Visualisation is a powerful technique used in these systems that provides an indispensable step in assisting end-users to understand and interpret the data. In this paper, we present a systematic review to synthesise literature related to the use of visualisation techniques in the domain of agriculture. The search identified 61 eligible articles, from which we established end-users, visualisation techniques and data collection methods across different application domains. We found visualisation techniques used in various areas of agriculture, including viticulture, dairy farming, wheat production and irrigation management. Our results show that the majority of DSSs utilise maps, together with satellite imagery, as the central visualisation. Also, we observed that there is an excellent opportunity for dashboards to enable end-users with better interaction support to understand the uncertainty of data. Based on this analysis, we provide design guidelines towards the implementation of more interactive and visual DSSs.

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Section: Viticulturementioning

confidence: 99%

A review of visualisations in agricultural decision support systems: An HCI perspective

Gutiérrez

Htun

Schlenz³

et al. 2019

Computers and Electronics in Agriculture

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“…Implementation of digital farming practices result in a sustainable, efficient, and stable production with a significant increase in yield. Some of the technologies involved in digital farming include the Internet of Thing [180] , big data analysis [181] , smart sensors [182] , GPS and GIS, ICT [183] , wireless sensor networks [184,185] , UAV [186][187][188] , cloud computing [189][190][191] , simulation software [192][193][194][195] , mapping applications [196,197] , virtual farms [198][199][200] , mobile devices [201][202][203][204] , and robotics. A conceptual illustrating of digital farming and its relationship with agricultural robotics is provided in Figure 6, showing that the collected data by the robot agents are sent to a cloud advisory center for decision makings.…”

Section: Agricultural Robotics and Digital Farmingmentioning

confidence: 99%

Research and development in agricultural robotics: A perspective of digital farming

Shamshiri¹,

Weltzien²,

Hameed³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.

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“…In addition to remote and proxy data collected from distinct sensors, the use of mobile devices with multitag technologies (Cunha et al, 2010;Luvisi et al, 2011) facilitates the recording of a great wealth of data. These numerous spatial data have stimulated new developments in both software and hardware, jointly with statistical processing which stems from geostatistics, image pattern recognition and satellite image processing and which includes machine learning.…”

Section: The Issue Of Big-data Handling and The Statistical Processinmentioning

confidence: 99%

An overview of the recent approaches to terroir functional modelling, footprinting and zoning

Vaudour

Costantini²,

Jones

et al. 2015

SOIL

102

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Abstract. Notions of terroir and their conceptualization through agro-environmental sciences have become popular in many parts of world. Originally developed for wine, terroir now encompasses many other crops including fruits, vegetables, cheese, olive oil, coffee, cacao and other crops, linking the uniqueness and quality of both beverages and foods to the environment where they are produced, giving the consumer a sense of place. Climate, geology, geomorphology and soil are the main environmental factors which make up the terroir effect on different scales. Often considered immutable culturally, the natural components of terroir are actually a set of processes, which together create a delicate equilibrium and regulation of its effect on products in both space and time. Due to both a greater need to better understand regional-to-site variations in crop production and the growth in spatial analytic technologies, the study of terroir has shifted from a largely descriptive regional science to a more applied, technical research field. Furthermore, the explosion of spatial data availability and sensing technologies has made the within-field scale of study more valuable to the individual grower. The result has been greater adoption of these technologies but also issues associated with both the spatial and temporal scales required for practical applications, as well as the relevant approaches for data synthesis. Moreover, as soil microbial communities are known to be of vital importance for terrestrial processes by driving the major soil geochemical cycles and supporting healthy plant growth, an intensive investigation of the microbial organization and their function is also required. Our objective is to present an overview of existing data and modelling approaches for terroir functional modelling, footprinting and zoning on local and regional scales. This review will focus on two main areas of recent terroir research: (1) using new tools to unravel the biogeochemical cycles of both macro-and micronutrients, the biological and chemical signatures of terroirs (i.e. the metagenomic approach and regional fingerprinting); (2) terroir zoning on different scales: mapping terroirs and using remoteand proxy-sensing technologies to monitor soil quality and manage the crop system for better food quality.

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Virtual vineyard for grapevine management purposes: A RFID/GPS application

Cited by 14 publications

References 11 publications

A review of visualisations in agricultural decision support systems: An HCI perspective

A review of visualisations in agricultural decision support systems: An HCI perspective

Research and development in agricultural robotics: A perspective of digital farming

An overview of the recent approaches to terroir functional modelling, footprinting and zoning

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