MIKIL - Multiskalige Integration moderner KI-Technologie in dezentralen Landwirtschaftssystemen (NaLamKI-Subproject)
Project duration:
01.01.2021 to 31.12.2023
Project description:
In the NaLamKI joint project, sensor and machine data from remote sensing, soil sensors, robotics, manual data collection and inventory data are aggregated in order to optimise agricultural processes such as irrigation, fertilisation and pest control more sustainably, efficiently and transparently using modern AI methods and (partially) autonomous vehicle and agent networks. NaLamKI creates the possibility of a high-dimensional evaluation and fusion of all aggregated data. GAIA-X compliant services are made available in a decentralised and interoperable manner while preserving the data sovereignty of the actors.
For the University of Hohenheim, the MIKIL sub-project focuses on the integration of modern AI methods at all levels of the NaLamKI system. In addition to the collaborative development of an overarching complex system architecture for a GAIA-X-compliant distributed agricultural system based on determined domain-specific requirements, this includes in particular the AI-based processing, fusion and analysis of the data collected and available in the project at different levels, as well as the federated knowledge exchange between cooperating machines and subsystems.
Scientists involved on site:
- Jonathan Heil, M.Sc. cand.
- Mortesa Hussaini, M.Sc.
- Jun.-Prof. Dr. Anthony Stein
Departments involved on site:
- Fg. 440g Artificial Intelligence in Agricultural Engineering
Consortium of the research network:
- John Deere GmbH & Co. KG (Consortium leader)
- Fraunhofer Heinrich-Hertz-Institut
- Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI) Kaiserslautern
- Technische Universität Kaiserslautern
- Julius-Kühn-Institut
- OptoPrecision GmbH
- Robot Makers GmbH
- Planet Labs Germany GmbH
- NT Neue Technologien AG
- Deutsche Landwirtschafts-Gesellschaft DLG e.V.
- Förderverein Digital Farming (FDF)
- Landwirtschaftliche Lehr- und Versuchsanstalt Hofgut Neumühle
Funding provider:
- Bundesministerium für Wirtschaft und Energie (BMWi)
- Förderkennzeichen: 01MK21003J
More Information:
Projectwebsite: https://nalamki.de/
KINERA - Künstliche Intelligenz für eine effiziente und resiliente Agrartechnik
Project duration:
19.04.21 to 18.04.2024
Project description:
The targeted use of artificial intelligence methods is intended to increase the efficiency and resilience of procedural processes in agricultural technology. In particular, the process of crop establishment is being examined as a real-life experiment using a conventional tractor-implement combination and autonomously with a robot. The aim is to achieve easier operability through setting support or self-adaptation and thus to specifically reduce the performance gap of the machine. The machine should also access information through communication with other machines and the cloud. In the context of machine communication, the potential of swarm robotics is simulated. The reliability of the system is ensured by a three-layer, fault-tolerant information and system architecture with integration of a farm server.
The utilisation of a sowing combination is increased while at the same time the machine operator is relieved by easier operability. This is measurable through the development of a prototype by retrofitting a conventional drill combination and embedding the machine in a developed system architecture. The three-layer system architecture becomes resilient to external disturbances such as network failures through the integration of a farm server at the operating level, and the integration of the cloud level addresses possibilities for process optimisation on the tactical planning horizon as well as the integration of additional information sources. The simulative evaluation of swarm robotics will show possible potentials of scalable robots in the agricultural context.
The Department of Artificial Intelligence in Agricultural Engineering is developing the required AI methods in the project. For this purpose, methods for observing the environment from sensor data as well as methods for the automated adaptation of machine configurations are being developed. In addition, simulative AI methods are being developed specifically for the use of robots and the potential of swarm robotics is being explored.
Scientists involved on site:
- Jonas Boysen, M.Sc.
- Georg Feyrer, M.Sc.
- Jun.-Prof. Dr. Anthony Stein
Departments involved on site:
- Fg. 440g Artificial Intelligence in Agricultural Engineering
- Fg. 440d Process Engineering in Plant Production
- Chair of Business Informatics
Consortium of the research network:
- Exatrek, EXA Computing GmbH
- Smart Site Solutions GmbH
- CLAAS Vertriebsgesellschaft mbH
- Horsch Maschinen GmbH
- VDMA Landtechnik
Funding provider:
- Bundesministerium für Ernährung und Landwirtschaft (BMEL)
- Bundesanstalt für Landwirtschaft und Ernährung (BLE)
NOcsPS - Landwirtschaft 4.0 ohne chemisch-synthetischen Pflanzenschutz / VP25 KI-basierte Hyperspektraldatenanalyse für ein effizientes Pflanzenpathogenmonitoring
Project duration:
01.04.2022 to 30.11.2023
Project description:
A sufficient and high-quality food and biomass supply that is produced in an even more environmentally and nature-friendly manner is a strong socio-political concern. The use of synthetic chemical pesticides (csPSM) is increasingly criticised due to residues in food and nature as well as threats to biodiversity.
This means that an Agriculture 4.0 can be established that follows biological principles using the latest networked technologies, while dispensing with csPSM. At the same time, the use of mineral fertilisers is made possible to ensure soil fertility to produce the required amount of biomass yields.
This approach represents a complete reorientation in arable farming and requires careful accompanying research from all angles and at all scales.
The aim of the research network of the University of Hohenheim (UHOH) and Georg-August University Göttingen (UGOE) and the Julius Kühn Institute (JKI) is to develop, analyse and describe NOcsPS cultivation systems in comparison with other cultivation systems. This comparison is carried out in system, exact and on-farm trials at plot, field, farm and landscape level as well as from an ecological, economic and social perspective.
Our part in this project:
AI-based hyperspectral data analysis for automated and efficient plant pathogen detection
A fundamental change in the application of chemical-synthetic crop protection must be accompanied by an increase in the level of automation and reliability in pest monitoring. The application of alternatives to chemical-synthetic crop protection requires a more detailed knowledge of the prevailing pathogen-induced stress in the field as well as the most precise localisation of the occurrence. Monitoring of this information with high temporal and spatial resolution can be made possible by means of field flights by UAVs (unmanned aerial vehicles, often also "drones") in combination with AI-based data analysis.
Based on novel systematic development and operationalisation principles for data-centric AI or machine learning applications (MLOps), an analysis pipeline is developed for the purpose of automated pathogen detection based on hyperspectral data. First, the existing database and the underlying pathogen monitoring process are analysed manually (WP 1: Process & Data Understanding). Subsequently, necessary data pre-processing steps and procedures for data augmentation are elicited according to the determined characteristics of the existing hyperspectral data and those to be further collected, and the technical (i.e. programmatic) development of the AI pipeline is initiated (WP 2: Data Engineering). This is followed by the informed and systematic selection and configuration of suitable Deep Learning models from the state of the art in machine vision, which are carried out in an iterative model development and validation process (WP 3: Model Engineering). The final part of the outlined work programme is the iterative technical implementation of the AI pipeline, which ensures both continuous systematic data management and, as a consequence, continuous model adaptation to newly available data (WP 4: MLOps).
At the end of the project, a robust and automated pathogen detection pipeline should exist, which can be adapted to varying conditions of use in a data-efficient manner and can reduce future costs for the essential task of pathogen monitoring in integrated crop protection through the use of AI.
Scientists involved on site:
- Georg Feyrer, M.Sc.
- Jun.-Prof. Dr. Anthony Stein
Departments involved on site:
- Fg. 440g Artificial Intelligence in Agricultural Engineering
Consortium of the research network:
Funding provider:
- Bundesministerium für Bildung und Forschung (BMBF)
More Information:
AIDAHO - AI & Data Science Certificate Hohenheim
Project duration:
01.12.2021 to 30.11.2025
Website:
Project description:
The steadily increasing relevance of artificial intelligence (AI) is fuelling an ever-growing need for qualified specialists. In order to meet this need, the federal government decided on a strategy to specifically promote the education of students with regard to their qualification in the field of artificial intelligence. The Federal-Länder programme "Artificial Intelligence in Higher Education" is an initiative that aims to promote the widespread implementation of AI as study content.
As part of this initiative, the AI & Data Science Certificate Hohenheim (AIDAHO) project is being funded at the University of Hohenheim. The aim of this project is to offer interdisciplinary courses to teach basic knowledge in the areas of AI, data science and scientific computing. The concrete implementation will be realised from the coming winter semester 2022/23 within the framework of an extracurricular qualification programme. It will comprise about 30 ECTS and will be certified after successful participation. In particular, students in advanced Bachelor's and Master's programmes are to be addressed. The long-term vision of this project is the development of an independent degree programme based on the teaching content of the qualification programme. The project thus ties in with the basic concept of the Computational Science Lab (CSL), which was founded in 2018 with the intention of creating cross-faculty networking opportunities around the topic of "digital transformation". The interdisciplinarity of the project is particularly reflected in the various project partners. From the CSL, in addition to our department for "Artificial Intelligence in Agricultural Engineering", the departments for "Econometrics and Business Statistics", "Food Informatics" and "Communication Science" are also involved.
The Department of Artificial Intelligence in Agricultural Engineering is responsible for the sub-project management of the core area of artificial intelligence, in particular machine learning. Initially, the focus will be on conveying fundamental basic knowledge in the subject areas of machine learning, as well as in explainable AI. Building on these fundamentals, the focus is then placed on specialisation in various sub-areas, such as deep learning in agricultural science issues, and thus on the practical application of the AI knowledge learned.
Scientists involved on site:
- Nils Kaul, M.Sc.
- Jun.-Prof. Dr. Anthony Stein
Departments involved on site:
- Fg. 440g Artificial Intelligence in Agricultural Engineering
- Fg. 520k Econometrics and Economic Statistics
- Fg. 150l Food Informatics
- Fg. 540a Communication Science, especially Media and Usage Research
Computational Science Lab (CSL)
Funding provider:
- Bundesministerium für Bildung und Forschung (BMBF)
HoPla - Hochleistungssensorik für smarte Pflanzenschutzbehandlung
Project duration:
01.09.22 to 31.08.25
Project description:
In order to ensure the supply of food for a growing world population, it must be produced ever more efficiently. At the same time, a greater burden on the environment must be avoided. Among other things, the use of plant protection products must be reduced to the absolutely necessary level. Herbicides, for example, are currently applied across the board, although the targeted treatment of unwanted plants could reduce their use by up to 70 percent without reducing yields.Goals and approach
In order to be able to apply herbicides in a more targeted manner and in lower quantities, crops and undesirable plants must be distinguished from each other during application.
The HOPLA project aims to make this possible by using fast camera sensors in conjunction with neural networks. Crops as well as undesirable plants are to be reliably detected while a field sprayer is driving over the arable land. The data is made available in real time for controlling the field sprayer. In this way, targeted treatment of undesirable plants should be possible and the amount of herbicide applied should be significantly reduced.
If the work is successfully completed, the camera-based detection system will be integrated into a field sprayer and evaluated in field trials. If successful, at the end of the development a new type of agricultural device will be available that allows for an on-demand treatment of undesirable plants with herbicides. The environmental impact of applying such substances could thus be drastically reduced.
Source: www.photonikforschung.de/projekte/sensorik-und-analytik/projekt/hopla.html (21.11.2022)
Scientists involved on site:
- Ahmet Saltik, M.Sc.
- Sourav Modak, M.Sc.
- Jun.-Prof. Dr. Anthony Stein
Project Management on site:
- Jun.-Prof. Dr. Anthony Stein
Departments involved on site:
- Fg. 440g Artificial Intelligence in Agricultural Engineering
Consortium of the research network:
- Robert Bosch GmbH, Stuttgart (coordination)
- University of Hohenheim, Institute for Agricultural Engineering, Stuttgart
- BASF Digital Farming GmbH, Cologne
- AMAZONEN-WERKE H. Dreyer SE & Co. KG (actively associated)
Funding provider:
- Bundesministerium für Bildung und Forschung (BMBF)
- Projektträger VDI-Technologiezentrum
- FKZ: 13N16327
More Information:
https://www.photonikforschung.de/projekte/sensorik-und-analytik/projekt/hopla.html
EmiMod - Weiterentwicklung von Methoden zur Erfassung, Modellierung und Beurteilung des Emissionsgeschehens in Nutztierställen
Project duration:
01.07.2023 to 31.12.2026
Project desription:
The aim of the research project is to further develop methods for determining emission rates from livestock farming. In particular, the data basis for a proper assessment of animal welfare stables with free ventilation and outdoor runs with regard to emissions is to be improved. The focus is on simplifying the investigation methodology and developing suitable assessment procedures, differentiated for various investigation purposes (emission factors, emission reduction performance, emission monitoring for practical use).
The revision and adaptation of the measurement strategies is carried out in an iterative process based on the collected measurement data combined with findings from mechanistic modeling, numerical flow simulations and artificial intelligence (AI) applications.The simplified measurement methods developed as part of the project are to be applied in the practice of emission measurement. The detailed description of the measurement procedures in the form of a measurement method manual and the intended procedure for assessing the emission potential of animal welfare stables will be communicated to the (specialist) public via a communication strategy and a web application. In addition, the data and project results will be published in the life sciences repository and made available for further research purposes via this platform.
The Artificial Intelligence in Agricultural Engineering department is responsible for the work package for establishing AI applications. Relevant use cases for the establishment of AI methods include:
- AI-based object detection for the detection and localization of urine puddles, with subsequent frequency and geometry estimation (excretion behavior)
- Image-based derivation of the urine puddle temperature from thermal image information as an indicator of the "age" of the urine puddles
- Continuous monitoring of functional areas and recording of the animals' areas of residence (resting and activity behavior)
- Classification of the animals' posture using AI-based pose recognition before or during the urination process
The image and video information is recorded by both thermal imaging (IR) cameras and RGB (surveillance) cameras, which are used in the pig and cattle barns. These are connected to a suitable IT system architecture, which initially pursues an edge computing concept for decentralized implementation of the AI solution. The work programme within this work package is based on novel principles for the systematic development of data-centric AI and machine learning (ML) solutions, which focus on operationalization, i.e. the transfer from the laboratory to the production environment. These principles are known as MLOps.
Consortium of the research network:
- Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB), Abteilung Technik in der Tierhaltung
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (BAuA), Gruppe Biologische Arbeitsstoffe
- Kuratorium für Technik und Bauwesen in der Landwirtschaft e. V. (KTBL)
- LWK Niedersachsen – LUFA Nord-West
- Bayerische Landesanstalt für Landwirtschaft (LfL), Institut für Landtechnik und Tierhaltung, Abteilung Emissionen und Immissionsschutz
- Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (LfULG), Abteilung Landwirtschaft
- Johann Heinrich von Thünen Institut, Bundesforschungsinstitut für Ländliche Räume, Wald und Fischerei (TI)
- Rheinische Friedrich-Wilhelms-Universität Bonn, Institut für Landtechnik
- Christian-Albrechts-Universität zu Kiel, Institut für Landwirtschaftliche Verfahrenstechnik (ILV)
Scientists involved on site:
- Simon Mielke, M.Sc.
- Anita Kapun, M.Sc.
- Jun.-Prof. Dr. Anthony Stein
- apl.-Prof. Dr. Eva Gallmann
Project Management on site:
- apl.-Prof. Dr. Eva Gallmann
- Jun.-Prof. Dr. Anthony Stein (AP 9 - Establishment of AI applications)
Departments involved on site:
- Fg. 440b Verfahrenstechnik der Tierhaltungssysteme
- Fg. 440g Künstliche Intelligenz in der Agrartechnik
Funding provider:
- Bundesministerium für Ernährung und Landwirtschaft (BMEL)
- Bundesanstalt für Landwirtschaft und Ernährung (BLE)