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Collaborative project: Further development and validation of the adaptive control of the harvesting and cleaning organs in sugar beet harvesters on the basis of image-based quality assessment - subproject C (ARERO2)
Project
Project code: 281DP07C20
Contract period: 01.09.2020
- 31.03.2022
Budget: 39,383 Euro
Purpose of research: Applied research
Keywords: Kulturverfahren, cultivation, Modellierung, modeling, Zuckerrübe, sugar beet , Energieeffizienz, energy efficiency, Präzise Landwirtschaft, precision farming, Ackerbau, crop production, Landtechnik, agricultural engineering
The aim of the project is the further development of the control of the cleaning and harvesting organs of a self-propelled sugar beet harvester on the basis of quality characteristics. The basis for the development of the system is the investigation of the interdependencies between machine parameters and harvest quality. Based on the expert knowledge of the drivers, the machine settings are manipulated in field tests to change the harvest quality and the interactions can be derived from the test data. In addition to the machine settings, the harvest quality is also influenced by the environmental conditions, which is why experiments at several locations are necessary. The obtained data basis is then used for modelling in image processing and control. For the extraction of the quality features, the raw data are categorized by experts and then image processing algorithms for machine recognition are developed. Methods from the fields of machine learning and deep learning are used. Subsequently, the obtained quality data can be merged with the machine data and a model for machine control can be derived. For this purpose, regression models are created using machine learning methods and their mapping of the cause-and-effect relationships is compared with the measured data and expert knowledge to determine the required complexity. The described procedure has already been developed and tested within a previous funded project. The system is now to be extended, Implemented and evaluated in the planned project, so that after completion of the project the final development into a product and its market launch can take place. For this purpose, the main requirement is the extension of the data set so that the image processing models function independently of the environmental conditions and the system limits and the complexity of the control system can be defined and evaluated. The work shedule is divided into three work phases, which build on each other and are based on the exp
Section overview
Subjects
- Crop Production
- Computer science
Framework programme
Funding programme
Excutive institution
Funding institution
Project management agencies
Associated projects: ARERO2
- Collaborative project: Further development and validation of the adaptive control of the harvesting and cleaning organs in sugar beet harvesters on the basis of image-based quality assessment - subproject A
- Collaborative project: Further development and validation of the adaptive control of the harvesting and cleaning organs in sugar beet harvesters on the basis of image-based quality assessment - subproject B
