Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm.

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostenice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination.

Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310⁻1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage.

Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery.

Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images.

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Development of a Web-based GIS monitoring and environmental assessment system for the Black Sea: application in the Danube Delta area.

In this paper, the development of a Web-based GIS system for the monitoring and assessment of the Black Sea is presented. The integrated multilevel system is based on the combination of terrestrial and satellite Earth observation data through the technological assets provided by innovative information tools and facilities. The key component of the system is a unified, easy to update geodatabase including a wide range of appropriately selected environmental parameters. The collection procedure of current and historical data along with the methods employed for their processing in three test areas of the current study are extensively discussed, and special attention is given to the overall design and structure of the developed geodatabase. Furthermore, the information system includes a decision support component (DSC) which allows assessment and effective management of a wide range of heterogeneous data and environmental parameters within an appropriately designed and well-tested methodology. The DSC provides simplified and straightforward results based on a classification procedure, thus contributing to a monitoring system not only for experts but for auxiliary staff as well. The examples of the system's functionality that are presented highlight its usability as well as the assistance that is provided to the decision maker. The given examples emphasize on the Danube Delta area; however, the information layers of the integrated system can be expanded in the future to cover other regions, thus contributing to the development of an environmental monitoring system for the entire Black Sea.

Using Earth Observation to update a Natura 2000 habitat map for a wetland in Greece.

The European Habitats Directive 92/43/EEC has defined the need for the conservation of habitats and species with the adoption of appropriate measures. Within the Natura 2000 ecological network of special areas of conservation, natural habitats will be monitored to ensure the maintenance or restoration of their composition, structure and extent. The European Space Agency's GlobWetland project has provided remotely sensed products for several Ramsar wetlands worldwide, such as detailed land cover-land use, water cycle and inundated vegetation maps. This paper presents the development and testing of an operational methodology for updating a wetland's habitat map using the GlobWetland products, and the evaluation of the extent to which GlobWetland products have contributed to the habitat map updating. The developed methodology incorporated both automated and analyst-supervised techniques to photo-interpret, delineate, refine, and evaluate the updated habitat polygons. The developed methodology was proven successful in its application to the wetland complex of the Axios-Loudias-Aliakmon delta (Greece). The resulting habitat map met the European and Greek national requirements. Results revealed that GlobWetland products were a valuable contribution, but source data (enhanced satellite images) were necessary to discriminate spectrally similar habitats. Finally, the developed methodology can be modified for original habitat mapping.

Remote sensing and GIS techniques for selecting a sustainable scenario for Lake Koronia, Greece.

During recent decades, Lake Koronia has undergone severe degradation as a result of human activities around the lake and throughout the basin. Surface and groundwater abstraction and pollution from agricultural, industrial, and municipal sources are the major sources of degradation. Planning a restoration project was hampered by lack of sufficient data, with gaps evident in both spatial and temporal dimensions. This study emphasized various remote sensing and geographic information system techniques, such as digital image processing and geographic overlay, to fill gaps using satellite imagery and other spatial environmental, hydrological, and hydrogeological data in the process of planning the restoration of Lake Koronia, following Ramsar guidelines. Current and historical remote sensing data were used to assess the current status and level of degradation, set constraints and define the ideotype for the restoration, and, finally, define and select the best restoration scenario.