Browsing by Subject "Classifier"

Now showing 1 - 2 of 2

Development and evaluation of a self-adaptable planting unit for an autonomous planting process of field vegetables
(2024) Lüling, Nils; Straub, Jonas; Stana, Alexander; Brodbeck, Matthias; Reiser, David; Berner, Pirmin; Griepentrog, Hans W.
Today, the number of solutions for automated processes in agriculture is growing rapidly. This is primarily driven by the lack of available and affordable labour, pricing pressures, and regulatory requirements. Vegetable production in particular has a lot of potential for automation, as many process steps, such as planting, are performed partly manually. Fully automated systems for the planting process are characterized by their big size, which is only suitable for large farms. At the same time, these planters typically have a low level of intelligence, which is essential for a fully autonomous planting process performed by autonomous vehicles or robots. The following work therefore deals with the development and construction of a prototype for vegetable planting via a robotic platform. This prototype is designed to meet the requirements of a conventional planter and carry out the planting process automatically using a robotic platform. To ensure a robust robotic planting process, an AI-based control system has been integrated that can detect and adjust the planting quality. For this reason, the planting unit was designed to allow dynamic changes in working depth and furrow width. By dynamically controlling these planting parameters, there is potential for a more sustainable planting process with lower energy requirements. A number of evaluations have been carried out to validate the described characteristics of the prototype planting unit.
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.