Browsing by Subject "Fuzzy Logik"

Now showing 1 - 2 of 2

Development of an automated sensor based system for weed harrowing in cereals
(2012) Rueda Ayala, Victor Patricio; Gerhards, Roland
The biggest challenge for weed harrowing in wheat and barley is to carry out a site-specific weed control according to the variability in conditions of soil, weeds and crop growth stage; selectivity of harrowing and yield response may also be considered. Therefore, an algorithm to automatically adjust the harrowing intensity was developed. First, different intensities were tested and the best results in terms of weed control efficacy and yield gains, were assigned as the optimal intensity levels. Second, a decision algorithm for weed harrowing was elaborated based assessments of leaf cover, weed density and soil density, to infer the output variable intensity. Third, we tested the system in two field experiments. The system requires more validation experiments in field with variable soil types and variable weed competition. Our perspective is that real-time intensity adjustment might be achievable if cameras are attached in the front and at the rear or sides of the harrow. Then feedback of the remaining weed competition might be used as new input to the model, which would indicate the necessity of cultivating a second or more passes.
Integrated technical approach for differentiated nitrogen application based on expert knowledge and multiple parameters
(2023) Heiß, Andreas; Griepentrog, Hans
Variable rate nitrogen (N) application is subject to spatio-temporal dynamics of multiple parameters and a high dependency on specific local conditions. Furthermore, existing algorithms are barely capable of considering agronomic expert knowledge and common application technology limits the precise in-field realization. This work approached the complexity of site-specific N management in terms of the decision making, as well as the technical and organizational realization in a systemic manner. A commercial real-time N-sensor system’s behavior was transferred into a fuzzy expert system and extended with soil information. The incorporation into a real-time control included also the spatial synchronization of dose rate determination and realization. A digital process chain to facilitate decision making, data management and execution in the field was conceptualized and evaluated with a prototypical implementation. The N-sensor’s algorithms were precisely imitated with a maximum percentage root mean square error of 0.14%, while the multi-parametric system has implied more robust decisions. In field tests, the real-time control has shown acceptable synchronization errors largely below 1 m and with medians in the range of 0.25 m under realistic conditions. The integrated system architecture has shown a high consistency in terms of straightforward and situative expert knowledge acquisition, as well as the suitability for different sensor and application technologies. The work represents a systemic approach for a derivation and employment of machine-readable algorithms from agronomic expert knowledge defining the cause-effect relationships for a site-specific N application. Its generic properties allow a supplementation by other models and can in turn strengthen them further.