Browsing by Subject "Decision support system"

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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.
Features and applications of a field imaging chlorophyll fluorometer to measure stress in agricultural plants
(2021) Linn, Alexander I.; Zeller, Alexander K.; Pfündel, Erhard E.; Gerhards, Roland
Most non-destructive methods for plant stress detection do not measure the primary stress response but reactions of processes downstream of primary events. For instance, the chlorophyll fluorescence ratio Fv/Fm, which indicates the maximum quantum yield of photosystem II, can be employed to monitor stress originating elsewhere in the plant cell. This article describes the properties of a sensor to quantify herbicide and pathogen stress in agricultural plants for field applications by the Fv/Fm parameter. This dedicated sensor is highly mobile and measures images of pulse amplitude modulated (PAM) chlorophyll fluorescence. Special physical properties of the sensor are reported, and the range of its field applications is defined. In addition, detection of herbicide resistant weeds by employing an Fv/Fm-based classifier is described. The PAM-imaging sensor introduced here can provide in-field estimation of herbicide sensitivity in crops and weeds after herbicide treatment before any damage becomes visible. Limitations of the system and the use of a classifier to differentiate between stressed and non-stressed plants based on sensor data are presented. It is concluded that stress detection by the Fv/Fm parameter is suitable as an expert tool for decision making in crop management.