Development of a stereovision-based technique to measure the spread patterns of granular fertilizer spreaders

Centrifugal fertilizer spreaders are by far the most commonly used granular fertilizer spreader type in Europe. Their spread pattern however is error-prone, potentially leading to an undesired distribution of particles in the field and losses out of the field, which is often caused by poor calibration of the spreader for the specific fertilizer used. Due to the large environmental impact of fertilizer use, it is important to optimize the spreading process and minimize these errors. Spreader calibrations can be performed by using collection trays to determine the (field) spread pattern, but this is very time-consuming and expensive for the farmer and hence not common practice. Therefore, we developed an innovative multi-camera system to predict the spread pattern in a fast and accurate way, independent of the spreader configuration. Using high-speed stereovision, ejection parameters of particles leaving the spreader vanes were determined relative to a coordinate system associated with the spreader. The landing positions and subsequent spread patterns were determined using a ballistic model incorporating the effect of tractor motion and wind. Experiments were conducted with a commercial spreader and showed a high repeatability. The results were transformed to one spatial dimension to enable comparison with transverse spread patterns determined in the field and showed similar results

Smart catheterization : a framework for real time catheter navigation system

Supplemen

A segmentation approach in novel real time 3D plant recognition system

One of the most invasive and persistent kind of weed in agriculture is also called "Broad-leaved Dock". The origin of the plant is Europe and northern Asia, but it has also been reported that this plant occurs in wide parts of Northern America. Eradication of this plant is labour-intensive and hence there is an interest in automatic weed control devices. Some vision systems were proposed that allow to localize and map plants in the meadow. However, these systems were designed and implemented for o-line processing. This paper presents a segmentation approach that allows for real-time recognition and application of herbicides onto the plant leaves. Instead of processing the gray-scale or colour images, our approach relays on 3D point cloud analysis and processing. 3D data processing has several advantages over 2D image processing approaches when it comes to extraction and recognition of plants in their natural environment

Automatisches Unkrautbehandlungssystem basierend auf 3D Pflanzenerkennung

Im Projekt SmartWeeder wird ein Prototyp eines sensorgestützten Unkrautbehandlungssystems für eine wirtschaftlichere und ökologischere Ampferbekämpfung entwickelt. Dieses System soll die automatische Anwendung einer physikalischen, chemischen oder thermischen Behandlungsmethode gezielt auf Einzelpflanzen ermöglichen, und damit die Produktivität, Wirtschaftlichkeit und Nachhaltigkeit der Unkrautregulierung erhöhen. Eine wichtige Anforderung an das System ist, dass die Ampferpflanze nicht in einem strukturierten Umfeld erkennt werden muss, sondern auf der grünen Wiese, in welcher sich die Ampferpflanze farblich nicht von der Umgebung unterscheidet (siehe Abbildung 1 links). Ziel des Projekt SmartWeeder ist es zu zeigen, dass in einer solchen Umgebung mittels drei-dimensionaler Daten-Erfassung und Auswertung eine genügende hohe Erkennungsrobustheit für den praktischen Einsatz erreicht werden kann

Sensors requirements for robust single plant detection in real time

This paper considers theoretical prerequisites for the sensor or sensor system that provides data for reliable single plant detection. The goal of the paper is to close the gap between the state of the art sensor technology and the theoretical “ideal” sensor to fulfill one and only purpose: providing single plant detection data in cluttered and disordered environments. Through analysis of artificial leaf forms, the paper determines expectations of the sensor spatial resolution and its maximum noise level. Supported by the results of a theoretical primer on predefined (artificial) shape analysis, an in-depth evaluation compares the results of 3D shape analysis with results obtained through 2D digital image processing. To analyze leaf shapes reliably, a minimum sensor resolution (2D and 3D) is determined by means of statistical feature analysis. As a final result of the paper, methods for sensor selection are pro-posed with regard to the specific task of analyzing leaf shape in real time

Analysis of necessary sensor spatial resolution for reliable plant detection

This paper considers theoretical prerequisites for a sensor or sensor system that provides data for reliable single plant detection. The goal of the paper is to close the gap between state of the art sensor technology and the theoretical “ideal” sensor to fulfill one and only purpose: providing single plant detection data in cluttered and disordered environments. Through analysis of artificial leaf forms, the paper determines expectations of the sensor spatial resolution and its maximum noise level, and is supported by the results of a theoretical primer on predefined(artificial) shape analysis. Each shape is analyzed by elliptic Fourier descriptor invariants obtained from the shape boundary,which allow translation, rotation and scale invariant of each object. This paper analyzes the statistical significance of each descriptor and its separability from other features. Finally an approximation formula developed here can be used to compute required resolution of such sensor for the plant detection