Die Landwirtschaft ist bereits digital – seit vielen Jahren prägen Informatik und Elektronik den landwirtschaftlichen Alltag. Digitale Anwendungen helfen beim Pflanzenschutz und der Wettervorhersage. Für die Präzisionslandwirtschaft sind Landmaschinen mit intelligenten Technologien bestückt. So kommunizieren sie untereinander. Automatisierte Arbeitsprozesse sind auf dem Feld und im Hof angekommen. Wie lässt sich Ökonomie und Ökologie nachhaltig verbinden und gut mit dem Faktor „Mensch“ integrieren? Die Frage, wer über Daten verfügt und sie interpretieren kann, wird zum Wettbewerbsfaktor in der Landwirtschaft – ist aber auch fachlich, juristisch und ethisch von Interesse. Diesen Themen widmet sich unter Schirmherrschaft des Bundeslandwirtschaftsministeriums (BMEL) die erste Konferenz zur Digitalisierung in der Landwirtschaft. Dabei geht es neben Begriffsfindung (z.B. Digitalisierung, Transformation, Big Data, Farming 4.0, Precision Farming), um das Aufzeigen der Potenziale und Risiken sowie den Erfahrungsaustausch über praxisnahe Lösungsansätze für die in der Wertschöpfungskette Beteiligten. Die Veranstaltung wird im Rahmen eines Programmkomitees durch DLG, KTBL, Leibniz Institut für Agrartechnik Potsdam, Bornim, Hochschule Osnabrück, Gesellschaft für Informatik in der Landtechnik und das Institut für Naturstofftechnik der TU Dresden unterstützt. Die Veranstaltung findet parallel zum IEEE 5G Summit statt (https://5glab.de/5gsummit/). Dies erlaubt erste Blicke auf die Anwendungsdemonstrationen der nächsten Mobilfunkgeneration und den direkten Kontakt mit den Teilnehmern des Summits während der Abendveranstaltung.:29. September 2016 Natur als Ressource, Konsumgesellschaft und Langzeitverantwortung – bioethische Fragestellungen zur informationstechnischen Durchdringung zukünftiger Bioökonomie. Bernhard Irrgang, TU Dresden Technikphilosophie Autonomes Fahren ist der Trend der Zukunft – Synergien zwischen Automotive und Offroad / Agrartechnik Dirk Geyer, AVL Software and Functions GmbH Anwendungsfelder der Digitalisierung (in der Tierhaltung) Detlef May, Lehr- und Versuchsanstalt für Tierzucht und Tierhaltung e.V. Groß Kreutz Vernetztes autonomes Fahren Frank Fitzek, TU Dresden - 5GLab Germany Anforderungen an ein Farm Management System Peer Leithold, Agricon Digitalisierung und Big Data Thomas Engel JD Technology & Innovation Center Kaiserslautern Beitrag und Erwartungen der Erzeugerverbände an die Digitalisierung der Landwirtschaft Bianca Lind Geschäftsführerin, Arbeitsgemeinschaft Deutscher Rinderzüchter e.V (ADR) Aspekte der Datennutzung in der Wertschöpfungskette in der Landwirtschaft Ines Härtel, Europa-Universität Viadrina Frankfurt (Oder) Abendveranstaltung und Dinner Speech Bernhard Polten, Bundesministerium für Ernährung und 30.September 2016 Grußwort Thomas Schmidt, Sächsischer Staatsminister für Umwelt und Landwirtschaft Future Farming – Potenziale und Risiken der Digitalisierung Hubertus Paetow, DLG Schritte einer digitalen (R)Evolution bei CLAAS Andreas Wübbeke / Carsten Hoff, Claas E Systems Partnerschaft durch Digitalisierung – Onlinelösungen zur optimierten Zusammenarbeit in der Wertschöpfungskette Matthias Schulte /Markus Reiners, Nordzucker AG Landwirtschaft 4.0 rückt näher, dank der individuellen und herstellerübergreifenden Datenaustauschplattform Jens Möller / Johannes Sonnen; DKE GmbH Vernetzung landwirtschaftlicher Prozesse - Know How nutzbar machen Karl-Heinz Krudewig, 365FarmNet Group GmbH Open(Geo-)Data - ein Katalysator für die Digitalisierung in der Landwirtschaft?\" Olaf Nölle, Disy Informationssysteme GmbH Data Warehouse für boden- und agrarwissenschaftliche Forschungsdaten Uwe Heinrich, Leiter BonaRes-Datenzentrum, ZALF e.V. Beitrag des bitkom zur Umsetzung der Digitalisierung in der Landwirtschaft Miriam Tänzer, bitkom Abschlussvortrag - Auf dem Weg zum Internet der Felder und Pflanzen Amos Albert, Bosch Start-up Deepfield Robotics Marktplatz- und Feedback Session

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There is a remarkable growth in the field of Information Communication Technology (ICT) in Developing Countries (DCs). Telecommunication is one of the areas where ICT is recording an ongoing rapid change. Mobile phones are becoming pervasive in daily scenario; and among the beneficiaries of this are farmers. Farmers are using mobile phones in executing their farming business and daily life. At the same time, Wireless Sensor Networks (WSNs) are also showing a result in developed part of our world. WSNs potential in sensing various environmental condition, their affordability and applicability motivated conducting of this master thesis. Therefore, the objective of conducting this master thesis is to investigate and identify how the use of mobile phones in conjunction with WSN enable farmers in Ethiopia monitor and control their farm field. We use firsthand qualitative data we gathered during our field work in Ethiopia to design our proposed prototype. Functional requirements and system design guideless are obtained from observation we make and interviews we carry out on irrigation based farmers around town of Meki in region of Oromia. We use our prototype to demonstrate and evaluate how irrigation based farmers benefit from existence of such system.

Tyrimo objektas – UAB ,,Dotnuvos projektai‘‘ technologinių inovacijų diegimas klientų ūkiuose. Tyrimo tikslas – nustačius problemas su kuriomis susiduria UAB ,,Dotnuvos projektai‘‘ klientai diegdami siūlomas inovacijas, pateikti technologinių inovacijų diegimo valdymo tobulinimo kryptis. Uždaviniai: 1. Išanalizuoti teorinius inovacijų klasifikavimo, jų diegimo valdymo aspektus; 2. Parengti inovacijų diegimo valdymo klientų ūkiuose tyrimo metodiką. 3. Nustatyti ir išanalizuoti inovacijų diegimo problemas bei galimybes UAB ,,Dotnuvos projektai‘‘ klientų ūkiuose. 4. Parengti inovacijų diegimo valdymo tobulinimo kryptis. Tyrimo metodai: mokslinės literatūros analizės ir sintezės, palyginimo, grafinio vaizdavimo apklausos metodai. Tyrimo rezultatai: o pirmoje darbo dalyje pateikta lietuvių bei užsienio autorių mokslinė analizė inovacijų sampratos ir esmės, klasifikavimo bei inovacijų valdymo klausimais. o antroje darbo dalyje supažindinta su UAB ,,Dotnuvos projektai‘‘ veikla taip pat pateiktas tyrimo detalus planas, kurio metu siekta nustatyti problemos ir galimybės diegiant inovacijas UAB ,,Dotnuvos projektai‘‘ klientų ūkiuose. o trečioje darbo dalyje pateikta apklausos rezultatų analizė, taip pat pateikta inovacijų diegimo valdymo tobulinimo kryptys. Tyrimo rezultatai skelbti studentų mokslinėje konferencijoje ,,Jaunasisis mokslininkas 2014‘‘. / Research object – installation of technological innovation by UAB ,,Dotnuvos projektai'' in clients' farms. Research aim – to indentify problems faced by clients of UAB ,,Dotnuvos projektai‘‘ in installation of proposed innovation, to propose the direction for technological innovation installation management improvements. Objectives: 1. To analyze theoretical aspects of innovations classification and installation management; 2. To prepare research methodology for innovation installation management in clients' farms; 3. To identify and analyze innovation installation problems and opportunities in UAB ,,Dotnuvos projektai'' client farms; 4. To prepare directions for innovation installation management inprovements. Research metods: synthesis and analysis of scientific literature, comparison, graphical representation, interview. Research results: o the first part of work presents concept, essence, classification and management of innovations as described in the Lithuanian and foreign scientific literature. o the second part of work introduces activities of UAB ,,Dotnuvos projektai‘‘, presents detailed research plan to analyse problems and oppurtunities in innovation installation in UAB ,,Dotnuvos projektai‘‘ clients' farms. o the third part of work analyses result of accomplished research, also proposes the directions of innovation installation management improvements. The research results are published at the students’ scientific conference: „Young Scientist 2014“.

Agricultura de Precisão é um novo paradigma de gestão da produção agrícola, que considera as variabilidades da produtividade e dos fatores de produção. Nesse novo paradigma de produção os agrônomos especializados em Agricultura de Precisão utilizam softwares para o gerenciamento de dados e visualização de mapas que são gerados nas diversas fases do processo. Estes softwares são caros e complexos, o que dificulta para os usuários não especialistas o acesso aos mapas, ficando o seu uso restrito apenas ao usuário especializado. o objetivo deste trabalho é o desenvolvimento de um ambiente, com interface amigável e de baixo custo, que opere pela internet, permitindo aos usuários menos especializados a visualização dos mapas e a inserção de informações indexadas ao talhões de produção em uma base de dados remota. Foi desenvolvida uma solução de software utilizando componentes de software livre como servido web Apache, a linguagem interpretada PHP, o módulo do Apache Mapscript e o Banco de Dados MySQL. Para a interação dos usuários com a imagem dos mapas foi proposto e desenvolvido um algorítmo para a criação de pontos clicáveis que são gerados dinamicamente. / Precision Agriculture is a new paradigm to manage yield variability and the agricultural inputs. The agricultural engineer specialized in Precision Farming uses softwares to manage data and to visualize maps that are made in the many steps of the process. These softwares are expensive and complex and, thus, dificult to those users who are not specialized in accessing the maps. The aim of this work is to develop a system, with a friendly-user and low cost interface, that operates through the internet, allowing unspecialized users to visualize thos maps and to insert field indexed information in a remote database. A software was developed using free components like Apache webserver, PHP script language, Mapscript Apache's module and the Mysql database. For user interaction with the map image, a algoritm to dinamicly create link poinst was proposed and developed.

This thesis deals with the new unconventional way of agricultural land management. Currently, high precision of work machines in the field is required. The development and accessibility of modern agricultural equipment linked to the information technology has led this branch to the unnecessary and high precise field ecosystem management. This new approach is called as precision agriculture. In the theoretical part of work is made the literature research and overview of the available data about the precision farming methods and the possibilities of field observation and data collection, including their processing with the available high information technology. The practical part evaluates the current possibilities of precision farming in the Czech Republic and in the world and looks for a solution to its implementation in the specific business environment of the agricultural laboratory.

Agricultura de Precisão é um novo paradigma de gestão da produção agrícola, que considera as variabilidades da produtividade e dos fatores de produção. Nesse novo paradigma de produção os agrônomos especializados em Agricultura de Precisão utilizam softwares para o gerenciamento de dados e visualização de mapas que são gerados nas diversas fases do processo. Estes softwares são caros e complexos, o que dificulta para os usuários não especialistas o acesso aos mapas, ficando o seu uso restrito apenas ao usuário especializado. o objetivo deste trabalho é o desenvolvimento de um ambiente, com interface amigável e de baixo custo, que opere pela internet, permitindo aos usuários menos especializados a visualização dos mapas e a inserção de informações indexadas ao talhões de produção em uma base de dados remota. Foi desenvolvida uma solução de software utilizando componentes de software livre como servido web Apache, a linguagem interpretada PHP, o módulo do Apache Mapscript e o Banco de Dados MySQL. Para a interação dos usuários com a imagem dos mapas foi proposto e desenvolvido um algorítmo para a criação de pontos clicáveis que são gerados dinamicamente. / Precision Agriculture is a new paradigm to manage yield variability and the agricultural inputs. The agricultural engineer specialized in Precision Farming uses softwares to manage data and to visualize maps that are made in the many steps of the process. These softwares are expensive and complex and, thus, dificult to those users who are not specialized in accessing the maps. The aim of this work is to develop a system, with a friendly-user and low cost interface, that operates through the internet, allowing unspecialized users to visualize thos maps and to insert field indexed information in a remote database. A software was developed using free components like Apache webserver, PHP script language, Mapscript Apache's module and the Mysql database. For user interaction with the map image, a algoritm to dinamicly create link poinst was proposed and developed.

In 2025, there will be almost 8 billion people to feed as the worlds population rapidly increases. To meet domestic and export demands, Australian grain productivity needs to approximately triple in the next 20 years, and this production needs to occur in an environmentally sustainable manner. The advent of Hi-tech Precision Farming in Australia has shown promise in recent time to optimize the use of resources. Most

Soil landscape characterization into landform elements for precision agriculture has become an important issue. As soil properties and crop yields change over the landscape, delineating landform elements as a basis for site-specific application of crop inputs has become a reality. Two different methods of delineating landform elements from agricultural fields were tested and compared. The first method delineated landform elements from digital elevation maps with the use of the LandMapR(tm) software, the second method delineated classes from IKONOS high resolution panchromatic images using an unsupervised classification algorithm. The LandMapR(tm) model delineated landform elements from true elevation data collected in the field and was considered the reference dataset to which the image classification maps were compared to. The IKONOS imagery was processed using a combination of one filtering algorithm and one unsupervised classification method prior to being compared to the classified DEM. A total of 20 filtering algorithms and two unsupervised methods were used for each of the five study sites. The study sites consisted of four agricultural fields covered with crop stubble and one field in summer fallow. Image classification accuracy assessment was reported as overall, producer’s and user’s accuracy as well as Kappa statistic. Results showed that filtering algorithms and classification methods had no effects on image classification accuracies. Highest classification accuracy of image map to landform element map comparison achieved for all study sites was 17.9 %. Classification accuracy was affected by the heterogeneity of the ground surface cover found in each field. However, the classification accuracy of the fallow field was not superior to the stubble fields. / May 2006

This study investigated the possibility of using data, acquired from airborne multi-spectral or hyper-spectral sensors, to detect nitrogen status and presence of weeds in crops; with the ultimate aim of contributing towards the development of a decision support system for precision crop management (PCM). / A 24-waveband (spectrum range 475 to 910 nm) multi-spectral sensor was used to detect weeds in corn (Zea mays L.) and soybean ( Glycine max (L.) Merr.) in 1999. Analysis of variance (ANOVA), followed by Scheffe's test, were used to determine which wavebands displayed significant differences in aerial spectral data due to weed treatments. It was found that the radiance values were mainly indicative of the contribution of weeds to the total vegetation cover in various plots, rather than indicative of changes in radiance of the crops themselves, or of differences in radiance between the weed populations and the crop species. / In the year 2000, a 72-waveband (spectrum range 407 to 949 nm) hyperspectral sensor was used to detect weeds in corn gown at three nitrogen levels (60, 120 and 250 kg N/ha). The weed treatments were: no control of weeds, control of grasses, control of broadleaved weeds and control of all weeds. Imagery was acquired at the early growth, tassel, and fully-mature stages of corn. Hyper-spectral measurements were also taken with a 512-waveband field spectroradiometer (spectrum range 270 to 1072 nm). Measurements were also carried out on crop physiological and associated parameters. ANOVA and contrast analyses indicated that there were significant (alpha = 0.05) differences in reflectance at certain wavebands, due to weed control strategies and nitrogen application rates. Weed controls were best distinguished at tassel stage. Nitrogen levels were most closely related to reflectance, at 498 nm and 671 nm, in the aerial data set. Differences in other wavebands, whether related to nitrogen or weeds, appeared to be dependent on the growth stage. Better results were obtained from aerial than ground-based spectral data. / Regression models, representing crop biophysical parameters and yield in terms of reflectance, at one or more wavebands, were developed using the maximum r2 criterion. The coefficients of determination (r 2) were generally greater than 0.7 when models were based on spectral data obtained at the tassel stage. Models based on normalized difference vegetation indices (NDVI) were more reliable at estimating the validation data sets than were the reflectance models. The wavebands at 701 nm and 839 nm were the most prevalent in these models. / Decision trees, artificial neural networks (ANNs), and seven other classifiers were used to classify spectral data into the weed and nitrogen treatment categories. Success rates for validation data were lower than 68% (mediocre) when training was done for all treatment categories, but good to excellent (up to 99% success) for classification into levels of one or the other treatment (i.e. weed or nitrogen) and also classification into pairs of levels within one treatment. Not one classifier was determined best for all situations. / The results of the study suggested that spectral data acquired from airborne platforms can provide vital information on weed presence and nitrogen levels in cornfields, which might then be used effectively in the development of PCM systems.