ICT tools for data management and analysis to support decisional process oriented to sustainable agri-food chains

Il settore agroalimentare sta affrontando delle sfide globali. La prima riguarda sfamare la popolazione mondiale che nel 2050, secondo le proiezioni delle Nazioni Unite, raggiungerà quota 9,3 miliardi di persone. La seconda sfida riguarda la richiesta da parte dei consumatori di prodotti ottenuti da filiere agroalimentari sempre più sostenibili, sicure e trasparenti. In particolare, l’Agricoltura sostenibile è una tecnica di gestione in grado di preservare la diversità biologica, la produttività, la capacità di rigenerazione, la vitalità e l’abilità alla funzione di un ecosistema agricolo, assicurandone, oggi e in futuro, le funzioni ecologiche, economiche e sociali a livello locale, nazionale ed globale, senza danneggiare altri ecosistemi. Quindi, per fronteggiare la sfida dell’agricoltura sostenibile, gli agricoltori devono aumentare la qualità e la quantità della produzione, riducendo l’impatto ambientale attraverso nuovi strumenti e nuove strategie di gestione. Questo lavoro analizza l’integrazione nel settore agroalimentare di alcune tecnologie e metodologie ICT per l’acquisizione, gestione e analisi dei dati, come la tecnologia RFID (Radio Frequency IDentification), i FMIS (Farm Management Information Systems), i DW (Data Warehouse) e l’approccio OLAP (On-Line Analytical Processing). Infine, l’adozione delle tecnologie ICT da parte di vere aziende è stata valutata attraverso un questionario. Al riguardo dell’adozione delle tecnologie RFID, questo lavoro analizza l’opportunità di trasferimento tecnologico relativo al monitoraggio e controllo dei prodotti agroalimentari tramite l’utilizzo di sensori innovativi, intelligenti e miniaturizzati. Le informazioni riguardanti lo stato del prodotto sono trasferite in tempo reale in wireless, come previsto dalla tecnologia RFID. In particolare, due soluzioni RFID sono state analizzate, evidenziando vantaggi e punti critici in confronto ai classici sistemi per assicurare la tracciabilità e la qualità dei prodotti agroalimentari. Quindi, questo lavoro analizza la possibilità di sviluppare una struttura che combina le tecnologie della Business Intelligence con i principi della Protezione Integrata (IPM) per aiutare gli agricoltori nel processo decisionale, andando a diminuire l’impatto ambientale ed aumentare la performance produttiva. L’IPM richiede di utilizzare simultaneamente diverse tecniche di protezione delle colture per il controllo dei parassiti e patogeni tramite un approccio ecologico ed economico. Il sistema di BI proposto è chiamato BI4IPM e combina l’approccio OLTP (On-Line Transaction Processing) con quello OLAP per verificare il rispetto dei disciplinari di produzione integrata. BI4IPM è stato testato con dati provenienti da vere aziende olivicole pugliesi. L’olivo è una delle principali colture a livello globale e la Puglia è la prima regione produttrice in Italia, con un gran numero di aziende che generano dati sull’IPM. Le strategie di protezione delle colture sono correlate alle condizioni climatiche, considerando la forte relazione tra clima, colture e parassiti. Quindi, in questo lavoro è presentato un nuovo e avanzato modello OLAP che integra il GSI (Growing Season Index), un modello fenologico, per comparare indirettamente le aziende agricole dal punto di vista climatico. Il sistema proposto permette di analizzare dati IPM di diverse aziende agricole che presentano le stesse condizioni fenologiche in un anno al fine di individuare best practices e di evidenziare e spiegare pratiche differenti adottate da aziende che lavorano in differenti condizioni climatiche. Infine, è stata effettuata un’indagine al fine di capire come le aziende agricole della Basilicata si raggruppano in funzione del livello di innovazione adottato. È stato utilizzato un questionario per domandare alle aziende se adottano strumenti ICT, ed eventualmente in quale processo produttivo o di management vengano usati. È stata quindi effettuata un’analisi cluster sui dati raccolti. I risultati mostrano che, usando il metodo di clustering k-means, appaiono due gruppi: gli innovatori e gli altri. Mentre, applicando la rappresentazione boxlot, si ottengono 3 gruppi: innovatori, utilizzatori precoci e ritardatari.The Agri-Food sector is facing global challenges. The first issue concerns feeding a world population that in 2050, according to United Nations projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food chains. In particular, the Sustainable agriculture is a management strategy able to preserve the biological diversity, productivity, regeneration capacity, vitality and ability to function of an agricultural ecosystem, ensuring, today and in the future, significant ecological, economic and social functions at the local, national and global scales, without harming other ecosystems. Therefore, to face the challenge of the sustainable agriculture, farmers need to increase quality and quantity of the production, reducing the environmental impact through new management strategies and tools. This work explores the integration of several ICT technologies and methodologies in the agri-food sector for the data acquisition, management and analysis, such as RFID technology, Farm Management Information Systems (FMIS), Data Warehouse (DW) and On-Line Analytical Processing (OLAP). Finally, the adoption of the ICT technologies by real farms is evaluated through a survey. Regarding the adoption of the RFID technology, this work explores an opportunity for technology transfer related to the monitoring and control of agri-food products, based on the use of miniaturized, smart and innovative sensors. The information concerning to the state of the product is transferred in real time in a wireless way, according to the RFID technology. In particular, two technical solutions involving RFID are provided, highlighting the advantages and critical points referred to the normal system used to ensure the traceability and the quality of the agri-food products. Therefore, this work explores the possibility of developing a framework that combines business intelligence (BI) technologies with Integrated Pest Management (IPM) principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with OLAP to verify adherence to the IPM technical specifications. BI4IPM is tested with data from real Apulian olive crop farms. Olive tree is one of the most important crop at global scale and Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate IPM data. The crop protection strategies are correlated to the climate conditions considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced OLAP model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions. Finally, a survey aimed at investigating how Lucania' farms cluster according to the level of innovation adopted was performed. It was used a questionnaire for asking if farms adopt ICTs tools and, in case, what type they involved in managing and/or production processes. It has been done a cluster analysis on collected data. Results show that, using k-means clustering method, appear two clusters: innovators, remaining groups. While, using boxplot representation, clustered three groups: innovators, early adopters and laggards

Focus - Un système OLAP pour l'analyse de données de lutte intégrée : application à la culture de l'olivier

The Agri-Food sector is facing global challenges. The first concerns feeding a world population that in 2050, according to UN projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food cains. The Integrated Pest Management (IPM) could be an important instrument to help farmers to face these challenges. The IPM requires the simultaneous use of different crop protection techniques for the control of pests through an ecological and economic approach. This work explores the possibility to develop a framework that combines the Information and Communication Technologies (ICTs) with the IPM principles, in order to support the farmers in the decisional process, improving environmental and production performances. The proposed ICT tool is On-Line Analytical Processing (OLAP), which allows performing analysis in the domain of time and space verifying for a single farm the respect of the IPM technical specifications.Le secteur agroalimentaire est confronté à des défis mondiaux. Le premier concerne l'alimentation d'une population mondiale qui, selon les prévisions de l'ONU, atteindra 9,3 milliards de personnes en 2050. Le deuxième défi est la demande des consommateurs pour des produits de haute qualité obtenus par des chaînes agroalimentaires plus durables, plus sûres et plus claires. La lutte intégrée (LI) pourrait constituer un instrument important pour aider les agriculteurs à faire face à ces défis. La LI requiert l'utilisation simultanée de différentes techniques de protection des cultures pour la lutte contre les organismes nuisibles par le biais d'une approche écologique et économique. Ce travail explore la possibilité de développer un cadre théorique combinant les technologies des systèmes d'information et de Business Intelligence (BI) avec les principes de la LI, afin d'aider les agriculteurs dans le processus décisionnel pour améliorer les performances environnementales et de production. L'outil BI proposé est un système de traitement analytique en ligne (OLAP), qui permet d'effectuer des analyses spatio-temporelles pour les données de LI

Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

Analytics and Intelligence for Smart Manufacturing

Digital transformation is one of the main aspects emerged by the current 4.0 revolution. It embraces the integration between the digital and physical environment,including the application of modelling and simulation techniques, visualization, and data analytics in order to manage the overall product life cycle

A abordagem POESIA para a integração de dados e serviços na Web semantica

Orientador: Claudia Bauzer MedeirosTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: POESIA (Processes for Open-Ended Systems for lnformation Analysis), a abordagem proposta neste trabalho, visa a construção de processos complexos envolvendo integração e análise de dados de diversas fontes, particularmente em aplicações científicas. A abordagem é centrada em dois tipos de mecanismos da Web semântica: workflows científicos, para especificar e compor serviços Web; e ontologias de domínio, para viabilizar a interoperabilidade e o gerenciamento semânticos dos dados e processos. As principais contribuições desta tese são: (i) um arcabouço teórico para a descrição, localização e composição de dados e serviços na Web, com regras para verificar a consistência semântica de composições desses recursos; (ii) métodos baseados em ontologias de domínio para auxiliar a integração de dados e estimar a proveniência de dados em processos cooperativos na Web; (iii) implementação e validação parcial das propostas, em urna aplicação real no domínio de planejamento agrícola, analisando os benefícios e as limitações de eficiência e escalabilidade da tecnologia atual da Web semântica, face a grandes volumes de dadosAbstract: POESIA (Processes for Open-Ended Systems for Information Analysis), the approach proposed in this work, supports the construction of complex processes that involve the integration and analysis of data from several sources, particularly in scientific applications. This approach is centered in two types of semantic Web mechanisms: scientific workflows, to specify and compose Web services; and domain ontologies, to enable semantic interoperability and management of data and processes. The main contributions of this thesis are: (i) a theoretical framework to describe, discover and compose data and services on the Web, inc1uding mIes to check the semantic consistency of resource compositions; (ii) ontology-based methods to help data integration and estimate data provenance in cooperative processes on the Web; (iii) partial implementation and validation of the proposal, in a real application for the domain of agricultural planning, analyzing the benefits and scalability problems of the current semantic Web technology, when faced with large volumes of dataDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Sustainability Information Services for Agri-Food Supply Networks : Closing Gaps in Information Infrastructures

Several global developments (e.g. diminishing production resources, growing demand for bio-energy) and numerous sector-wide crises (e.g. BSE, swine fever, dioxin) have led to a changing attitude of society towards the consequences of the agri-food system‘s activities for social, economic and environmental issues, captured in the term of sustainability. Consumers in their role as final customers, and as a consequence also enterprises within agri-food supply networks, show increasing interest in the characteristics of food, and in turn, on the availability of related information and guarantees. New solutions for determination and communication of sustainability are needed for the agri-food sector, covering single aspects of sustainability as well as sustainability in a broader sense, including social, economic and environmental issues. The present doctoral thesis introduces a structured approach for developing sustainability information services for agri-food supply networks and presents a framework that integrates these services into existing network-wide production and decision processes. The approach is presented using the example of European pork production and the three selected information domains food safety (representing social sustainability), quality (representing economic sustainability) and global warming potential (representing environmental sustainability). Resulting information reference models give an aggregated overview on information availability and exchange in European pork supply networks, additional information demands of possible service users and deficiencies in the existing information infrastructures. Integrated service solutions which are based on the identified information sources, demands and deficiencies are introduced to exemplify the approach. The thesis supports different stakeholders involved in agri-food production, such as service developers, enterprise decision makers and management consultants, in developing enterprise- and supply network-specific solutions which meet customers’ and consumers’ demands by providing appropriate sustainability information and guarantees.Nachhaltigkeitsinformationsdienste für Netzwerke der Agrar- und Ernährungswirtschaft - Eliminierung von Defiziten in Informationsinfrastrukturen Eine Vielzahl globaler Entwicklungen (z. B. abnehmende Produktionsressourcen, wachsender Bedarf an Bioenergie) und die zahlreichen sektorweiten Krisen der vergangenen Jahrzehnte (z. B. BSE, Schweinepest, Dioxin) haben zu einem Umdenken innerhalb der Gesellschaft hinsichtlich der sozialen, ökonomischen und ökologischen Auswirkungen der Lebensmittelproduktion geführt, die sich unter dem Begriff der Nachhaltigkeit zusammenfassen lassen. Konsumenten in ihrer Rolle als Endverbraucher, und infolgedessen auch Unternehmen in lebensmittelerzeugenden Netzwerken, zeigen ein zunehmendes Interesse an Eigenschaften von Lebensmitteln und somit auch an der Verfügbarkeit von entsprechenden Informationen und Garantien. Die Agrar- und Ernährungswirtschaft benötigt neue Lösungsansätze zur Bestimmung und Kommunikation der Nachhaltigkeit ihrer Produkte und Prozesse, die sowohl einzelne Aspekte der Nachhaltigkeit abdecken, als auch Nachhaltigkeit als Ganzes, indem soziale, ökonomische und ökologische Aspekte erfasst werden. Die vorliegende Arbeit stellt eine strukturierte Vorgehensweise zur Entwicklung von Nachhaltigkeitsinformationsdiensten für Netzwerke der Agrar- und Ernährungswirtschaft vor und beschreibt wie diese Informationsdienste in bestehende Produktions- und Entscheidungsprozesse integriert werden können. Die Vorgehensweise wird anhand der europäischen Schweinefleischerzeugung und den drei ausgewählten Anwendungsbeispielen Lebensmittelsicherheit (soziale Nachhaltigkeit), Qualität (ökonomische Nachhaltigkeit) und globale Erwärmung (ökologische Nachhaltigkeit) demonstriert. Die resultierenden Informationsreferenzmodelle geben einen aggregierten Überblick über die Informationsverfügbarkeit und den -austausch in europäischen schweinefleischerzeugenden Netzwerken, zusätzliche Informationsbedarfe von potentiellen Informationsdienstnutzern und Defizite in den bestehenden Informationsinfrastrukturen. Aufbauend auf den identifizierten Informationsquellen, -bedarfen und -defiziten werden integrierte Lösungsbeispiele vorgestellt, um die Vorgehensweise zu veranschaulichen. Die vorliegende Arbeit bietet unterschiedlichen, an der Agrar- und Lebensmittelproduktion beteiligten Interessengruppen, wie z. B. Informationsdienstentwicklern, Entscheidungsträgern in Unternehmen und Unternehmensberatungen, eine Hilfestellung bei der Entwicklung von unternehmens- und netzwerkspezifischen Lösungen, die es ermöglichen sollen sowohl Unternehmen innerhalb von lebensmittelerzeugenden Netzwerken als auch Konsumenten bedarfsgerechte Nachhaltigkeitsinformationen und -garantien bereitzustellen