UAV Cloud Platform for Precision Farming

A new application for Unmanned Aerial Vehicles comes to light daily to solve some of modern society’s problems. One of the mentioned predicaments is the possibility for optimization in agricultural processes. Due to this, a new area arose in the last years of the twentieth century, and it is in constant progression called Precision Farming. Nowadays, a division of this field growth is relative to Unmanned Aerial Vehicles applications. Most traditional methods employed by farmers are ineffective and do not aid in the progression and solution of these issues. However, there are some fields that have the possibility to enhance many agriculture methods, such fields are Cyber-Physical Systems and Cloud Computing. Given its capabilities like aerial surveillance and mapping, Cyber- Physical Systems like Unmanned Aerial Vehicles are being used to monitor vast crops, to gather insightful data thatwould take a lot more time if being collected by hand. However, these systems typically lack computing power and storage capacity, meaning that much of its gathered data cannot be stored and further analyzed locally. That is the obstacle that Cloud Computing can solve. With the possibility to offload computing power by sending the collected data to a cloud, it is possible to leverage the enormous computing power and storage capabilities of remote data-centers to gather and analyze these datasets. This dissertation proposes an architecture for this use case by leveraging the advantages of Cloud Computing to aid the obstacles of Unmanned Aerial Vehicles. Moreover, this dissertation is a collaboration with an on-going Horizon 2020 European project that deals with precision farming and agriculture enhanced by Cyber-Physical Systems.A cada dia que passa, novas aplicações para Veículos aéreos não tripulados são inventadas, de forma a resolver alguns dos problemas actuais da sociedade. Um desses problemas, é a possibilidade de otimização em processos agrículas. Devido a isto, nos últimos anos do século 20 nasceu uma nova área de investigação intitulada Agricultura de alta precisão. Hoje em dia, uma secção desta área diz respeito à inovação nas aplicações com recurso a Veículos aéreos não tripulados. A maioria dos métodos tradicionais usados por agricultores são ineficientes e não auxiliam nem a evolução nem a resolução destes problemas. Contudo, existem algumas áreas científicas que permitem a evoluçao de algumos métodos agrículas, estas áreas são os Sistemas Ciber-Físicos e a Computação na Nuvem. Dadas as suas capacidades tais como a vigilância e mapeamento aéreo, certos Sistemas Ciber-Físicos como os Veículos aéreos não tripulados estão a ser usados para monitorizar vastas culturas de forma a recolher dados que levariam muito mais tempo caso fossem recolhidos manualmente. No entanto, estes sistemas geralmente não detêm grandes capacidades de computação e armazenamento, o que significa que muitos dos dados recolhidos não podem ser armazenados e analisados localmente. É aí que a Computação na Nuvem é útil, com a possibilidade de enviar estes dados para uma nuvem, é possível aproveitar o enorme poder de computação e os recursos de armazenamento dos datacenters remotos para armazenar e analisar estes conjuntos de dados. Esta dissertação propõe uma arquitetura para este caso de uso ao fazer uso das vantagens da Computação na Nuvem de forma a combater os obstáculos dos Veículos aéreos não tripulados. Além disso, esta dissertação é também uma colaboração com um projecto Europeu Horizonte 2020 na área da Agricultura de alta precisão com recurso a Veículos aéreos não tripulados

Agri-Food Land Transformations and Immigrant Farm Workers in Peri-Urban Areas of Spain and the Mediterranean

Spain is a global hotspot of transformations of agri-food land systems due to changing production intensity, diets, urbanization, market integration, and climate change. Characteristic of the Mediterranean, these expanding intersections with the migration, livelihoods, and food security strategies of immigrant farm workers urge new research into the “who,” “how,” and “why” questions of the transformation of agri-food land systems. Addressing this gap, we communicate preliminary results from field research in the Granada and Madrid areas. We use a novel conceptual framework of linkages among distinct agri-food land systems and the roles and agency of immigrant farm workers. Preliminary results integrating a combined land- and labor-centric approach address: (1) how the recent and ongoing transformations of specific agri-food land systems are indicative of close links to inexpensive, flexible labor of immigrant farm workers; (2) how the connectivity among transformations of multiple distinct agri-food land systems can be related to the geographic mobility of immigrant farm workers and livelihoods (non-farm work, gendered employment, peri-urban residential location, labor recruitment); and (3) how the struggles for food and nutrition security among immigrant farm workers are indicative of links to local sites and networked agrobiodiversity. This study can help advance the nexus of migration-land research with expanding ethical, justice, and policy concerns of land system sciences in relation to the new suite of agri-food interest and initiatives.Fulbright Scholarship BoardBureau of Educational and Cultural Affairs in Spain and the U.S

Ensuring health and food safety from rapidly expanding wastewater irrigation in South Asia: BMZ final report 2005-2008

Wastewater irrigation / Institutions / Public health / Health hazards / Diseases / Cropping systems / Vegetables / Fodder / Livestock / Risk assessment / Economic evaluation / Surveys / GIS / Research priorities / South Asia / India / Pakistan / Hyderabad / Faisalabad / Musi River

Data, Metrics and Monitoring in CGIAR - a strategic study

This document contains the final report of the Panel together with the ISPC commentary. The Panel Report presents an analysis of the current activities within CGIAR concerning data, metrics and indicators, and offers a series of recommendations to address the key issues and challenges identified

Internet of Things in Agricultural Innovation and Security

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed

Development of a spatial data infrastructure for precision agriculture applications

Precision agriculture (PA) is the technical answer to tackling heterogeneous conditions in a field. It works through site specific operations on a small scale and is driven by data. The objective is an optimized agricultural field application that is adaptable to local needs. The needs differ within a task by spatial conditions. A field, as a homogenous-planted unit, exceeds by its size the scale units of different landscape ecological properties, like soil type, slope, moisture content, solar radiation etc. Various PA-sensors sample data of the heterogeneous conditions in a field. PA-software and Farm Management Information Systems (FMIS) transfer the data into status information or application instructions, which are optimized for the local conditions. The starting point of the research was the determination that the process of PA was only being used in individual environments without exchange between different users and to other domains. Data have been sampled regarding specific operations, but the model of PA suffers from these closed data streams and software products. Initial sensors, data processing and controlled implementations were constructed and sold as monolithic application. An exchange of hard- or software as well as of data was not planned. The design was focused on functionality in a fixed surrounding and conceived as being a unit. This has been identified as a disadvantage for ongoing developments and the creation of added value. Influences from the outside that may be innovative or even inspired cannot be considered. To make this possible, the underlying infrastructure must be flexible and optimized for the exchange of data. This thesis explores the necessary data handling, in terms of integrating knowledge of other domains with a focus on the geo-spatial data processing. As PA is largely dependent on geographical data, this work develops spatial data infrastructure (SDI) components and is based on the methods and tools of geo-informatics. An SDI provides concepts for the organization of geospatial components. It consists of spatial- and metadata in geospatial workflows. The SDI in the center of these workflows is implemented by technologies, policies, arrangements, and interfaces to make the data accessible for various users. Data exchange is the major aim of the concept. As previously stated, data exchange is necessary for PA operations, and it can benefit from defined components of an SDI. Furthermore, PA-processes gain access to interchange with other domains. The import of additional, external data is a benefit. Simultaneously, an export interface for agricultural data offers new possibilities. Coordinated communication ensures understanding for each participant. From the technological point of view, standardized interfaces are best practice. This work demonstrates the benefit of a standardized data exchange for PA, by using the standards of the Open Geospatial Consortium (OGC). The OGC develops and publishes a wide range of relevant standards, which are widely adopted in geospatially enabled software. They are practically proven in other domains and were implemented partially in FMIS in the recent years. Depending on their focus, they could support software solutions by incorporating additional information for humans or machines into additional logics and algorithms. This work demonstrates the benefits of standardized data exchange for PA, especially by the standards of the OGC. The process of research follows five objectives: (i) to increase the usability of PA-tools in order to open the technology for a wider group of users, (ii) to include external data and services seamlessly through standardized interfaces to PA-applications, (iii) to support exchange with other domains concerning data and technology, (iv) to create a modern PA-software architecture, which allows new players and known brands to support processes in PA and to develop new business segments, (v) to use IT-technologies as a driver for agriculture and to contribute to the digitalization of agriculture.Precision agriculture (PA) ist die technische Antwort, um heterogenen Bedingungen in einem Feld zu begegnen. Es arbeitet mit teilflächenspezifischen Handlungen kleinräumig und ist durch Daten angetrieben. Das Ziel ist die optimierte landwirtschaftliche Feldanwendung, welche an die lokalen Gegebenheiten angepasst wird. Die Bedürfnisse unterscheiden sich innerhalb einer Anwendung in den räumlichen Bedingungen. Ein Feld, als gleichmäßig bepflanzte Einheit, überschreitet in seiner Größe die räumlichen Einheiten verschiedener landschaftsökologischer Größen, wie den Bodentyp, die Hangneigung, den Feuchtigkeitsgehalt, die Sonneneinstrahlung etc. Unterschiedliche Sensoren sammeln Daten zu den heterogenen Bedingungen im Feld. PA-Software und farm management information systems (FMIS) überführen die Daten in Statusinformationen oder Bearbeitungsanweisungen, die für die Bedingungen am Ort optimiert sind. Ausgangspunkt dieser Dissertation war die Feststellung, dass der Prozess innerhalb von PA sich nur in einer individuellen Umgebung abspielte, ohne dass es einen Austausch zwischen verschiedenen Nutzern oder anderen Domänen gab. Daten wurden gezielt für Anwendungen gesammelt, aber das Modell von PA leidet unter diesen geschlossenen Datenströmen und Softwareprodukten. Ursprünglich wurden Sensoren, die Datenverarbeitung und die Steuerung von Anbaugeräten konstruiert und als monolithische Anwendung verkauft. Ein Austausch von Hard- und Software war ebenso nicht vorgesehen wie der von Daten. Das Design war auf Funktionen in einer festen Umgebung ausgerichtet und als eine Einheit konzipiert. Dieses zeigte sich als Nachteil für weitere Entwicklungen und bei der Erzeugung von Mehrwerten. Äußere innovative oder inspirierende Einflüsse können nicht berücksichtigt werden. Um dieses zu ermöglichen muss die darunterliegende Infrastruktur flexibel und auf einen Austausch von Daten optimiert sein. Diese Dissertation erkundet die notwendige Datenverarbeitung im Sinne der Integration von Wissen aus anderen Bereichen mit dem Fokus auf der Verarbeitung von Geodaten. Da PA sehr abhängig von geographischen Daten ist, werden in dieser Arbeit die Bausteine einer Geodateninfrastruktur (GDI) entwickelt, die auf den Methoden undWerkzeugen der Geoinformatik beruhen. Eine GDI stellt Konzepte zur Organisation räumlicher Komponenten. Sie besteht aus Geodaten und Metadaten in raumbezogenen Arbeitsprozessen. Die GDI, als Zentrum dieser Arbeitsprozesse, wird mit Technologien, Richtlinien, Regelungen sowie Schnittstellen, die den Zugriff durch unterschiedliche Nutzer ermöglichen, umgesetzt. Datenaustausch ist das Hauptziel des Konzeptes. Wie bereits erwähnt, ist der Datenaustausch wichtig für PA-Tätigkeiten und er kann von den definierten Komponenten einer GDI profitieren. Ferner bereichert der Austausch mit anderen Gebieten die PA-Prozesse. Der Import zusätzlicher Daten ist daher ein Gewinn. Gleichzeitig bietet eine Export-Schnittstelle für landwirtschaftliche Daten neue Möglichkeiten. Koordinierte Kommunikation sichert das Verständnis für jeden Teilnehmer. Aus technischer Sicht sind standardisierte Schnittstellen die beste Lösung. Diese Arbeit zeigt den Gewinn durch einen standardisierten Datenaustausch für PA, indem die Standards des Open Geospatial Consortium (OGC) genutzt wurden. Der OGC entwickelt und publiziert eine Vielzahl von relevanten Standards, die eine große Reichweite in Geo-Software haben. Sie haben sich in der Praxis anderer Bereiche bewährt und wurden in den letzten Jahren teilweise in FMIS eingesetzt. Abhängig von ihrer Ausrichtung könnten sie Softwarelösungen unterstützen, indem sie zusätzliche Informationen für Menschen oder Maschinen in zusätzlicher Logik oder Algorithmen integrieren. Diese Arbeit zeigt die Vorzüge eines standardisierten Datenaustauschs für PA, insbesondere durch die Standards des OGC. Die Ziele der Forschung waren: (i) die Nutzbarkeit von PA-Werkzeugen zu erhöhen und damit die Technologie einer breiteren Gruppe von Anwendern verfügbar zu machen, (ii) externe Daten und Dienste ohne Unterbrechung sowie über standardisierte Schnittstellen für PA-Anwendungen einzubeziehen, (iii) den Austausch mit anderen Bereichen im Bezug auf Daten und Technologien zu unterstützen, (iv) eine moderne PA-Softwarearchitektur zu erschaffen, die es neuen Teilnehmern und bekannten Marken ermöglicht, Prozesse in PA zu unterstützen und neue Geschäftsfelder zu entwickeln, (v) IT-Technologien als Antrieb für die Landwirtschaft zu nutzen und einen Beitrag zur Digitalisierung der Landwirtschaft zu leisten

3D MODELING OF TWO LOUTERIA FRAGMENTS BY IMAGE-BASED APPROACH

The paper presents a digital approach to the reconstruction and analysis of two small-sized fragments of louteria, a kind of large terracotta vase, found during an archaeological survey in the south of Sicily (Italy), in the area of Cignana near the Greek colony of Akragas (nowadays Agrigento). The fragments of louteria have been studied by an image-based approach in order to achieve high accurate and very detailed 3D models. The 3D models have been used to carry out interpretive and geometric analysis from an archaeological point of view. Using different digital tools, it was possible to highlight some fine details of the louteria decorations and to better understand the characteristics of the two fragments. The 3D models provide also the possibility to study and to document these archaeological finds in a digital environment

An internet of things enabled framework to monitor the lifecycle of Cordyceps sinensis mushrooms

Cordyceps sinensis is an edible mushroom found in high quantities in the regions of the Himalayas and widely considered in traditional systems of medicine. It is a non-toxic remedy mushroom and has a high measure of clinical medical benefits including cancer restraint, high blood pressure, diabetes, asthma, depression, fatigue, immune disorder, and many infections of the upper respiratory tract. The cultivation of this kind of mushroom is limited to the region of the Sikkim and to cultivate in the other regions of the country, they are need of investigation and prediction of cordyceps sinensis mushroom lifecycle. From the studies, it is concluded that the precision-based agriculture techniques are limitedly explored for the prediction and growth of Cordyceps sinensis mushrooms. In this study, an internet of things (IoT) inspired framework is proposed to predict the lifecycle of Cordyceps sinensis mushrooms and also provide alternate substrate to cultivate Cordyceps sinensis mushrooms in other parts of the country. As a part of lifecycle prediction, a framework is proposed in this study. According to the findings, an IoT sensor-based system with the ideal moisture level of the mushroom rack is required for the growth of Cordyceps sinensis mushrooms

Spatial modeling of personalized exposure dynamics: the case of pesticide use in small-scale agricultural production landscapes of the developing world

Background: Pesticide poisoning is a global health issue with the largest impacts in the developing countries where residential and small-scale agricultural areas are often integrated and pesticides sprayed manually. To reduce health risks from pesticide exposure approaches for personalized exposure assessment (PEA) are needed. We present a conceptual framework to develop a spatial individual-based model (IBM) prototype for assessing potential exposure of farm-workers conducting small-scale agricultural production, which accounts for a considerable portion of global food crop production. Our approach accounts for dynamics in the contaminant distributions in the environment, as well as patterns of movement and activities performed on an individual level under different safety scenarios. We demonstrate a first prototype using data from a study area in a rural part of Colombia, South America. Results: Different safety scenarios of PEA were run by including weighting schemes for activities performed under different safety conditions. We examined the sensitivity of individual exposure estimates to varying patterns of pesticide application and varying individual patterns of movement. This resulted in a considerable variation in estimates of magnitude, frequency and duration of exposure over the model runs for each individual as well as between individuals. These findings indicate the influence of patterns of pesticide application, individual spatial patterns of movement as well as safety conditions on personalized exposure in the agricultural production landscape that is the focus of our research. Conclusion: This approach represents a conceptual framework for developing individual based models to carry out PEA in small-scale agricultural settings in the developing world based on individual patterns of movement, safety conditions, and dynamic contaminant distributions. The results of our analysis indicate our prototype model is sufficiently sensitive to differentiate and quantify the influence of individual patterns of movement and decision-based pesticide management activities on potential exposure. This approach represents a framework for further understanding the contribution of agricultural pesticide use to exposure in the small-scale agricultural production landscape of many developing countries, and could be useful to evaluate public health intervention strategies to reduce risks to farm-workers and their families. Further research is needed to fully develop an operational version of the model

Machine learning and task disambiguation in hand-picked agriculture

Although GPS-based travel data has been studied by many mainly for automated travel mode detection, the area of activity mode detection during harvest still remains an open technical challenge. This thesis proposes and tests a pattern recognition approach to harvest mode recognition from GPS travel data collected from 4 volunteers for 2 days in Oxnard, California. Three profiles were created to characterize activities performed during harvest. Piecewise quadratic interpolation was used on smoothened data to detect segments in trips taken by workers. Trip segments are then evaluated with the different profiles to find the best fitting profiles and the associated optimal parameters. Results indicated that the proposed framework performs well under data discrepancies. Identification of different modes during harvest is of relevance for assessing productivity of different workers and addressing any mismatch in vehicle scheduling. In our assessment, this proof-of-principle study demonstrates a use case for using GPS data in disambiguating different activities conducted during harvest; scalability of the methodology remains a challenge - programming GPUs to take advantage of independence in the different processes has been proposed to reduce the code runtime