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

Cartographic Visualisation of Data Measured by Field Harvesters

Yield is one of the primary concerns for farmers, as it is the basis for their income and, among other impacts, influences subsidies and taxes. Field harvesters equipped with sensors and a GNSS (Global Navigation Satellite System) receiver provide detailed and spatially localised values, where the measurements from such sensors need to be filtered and subject to further processing, including interpolation, for follow-up visualisation, analysis and interpretation. These data, their processing and their application are some of the aspects of the precision agriculture concept. This paper describes the individual steps of processing the data acquired by harvesters, which especially include the spatial filtering of these data and their interpolation. We also proposed a scheme that summarises cartographic visualisation methods for these data (final data, as well as data from different processing steps). Methods of processing and cartographic visualisation were verified in the example of the Pivovárka field (Rostěnice farm, Czech Republic). Both 2D and 3D cartographic visualisations were created. Future development of the proposed concept is discussed in the conclusions

Prediction of Yield Productivity Zones from Landsat 8 and Sentinel-2A/B and Their Evaluation Using Farm Machinery Measurements

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations

Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery

Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations

The SensLog Platform – A Solution for Sensors and Citizen Observatories

Part 6: Information SystemsInternational audienceSensLog is an integrated server side Web based solution for sensor data management. SensLog consists of a data model and a server-side application which is capable of storing, analyzing and publishing sensor data in various ways. This paper describes the technical advancements of the SensLog platform. SensLog receives measured data from nodes and/or gateways, stores data in a database, pre-processes data for easier queries if desired and then publishes data through the system of web-services. SensLog is suitable for sensor networks with static sensors (e.g. meteorological stations) as well as for mobile sensors (e.g. tracking of vehicles, human-as-sensor). The database model is based on the standardized data model for observations from OGC Observations & Measurements. The model was extended to provide more functionalities, especially in the field of users’ hierarchy, alerts and tracking of mobile sensors. The latest SensLog improvements include a new version of the database model and an API supporting citizen observatories. Examples of pilot applications using SensLog services are described in the paper

Population distribution modelling at fine spatio-temporal scale based on mobile phone data

Population distribution modelling can benefit many different domains, for example, transportation, urban planning, ecology or emergency management. Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises. Mobile phone data represents relatively reliable and time accurate information on real-time population distribution, movement and behaviour. In this study, we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area (City of Brno, Czech Republic). Analysis is based on the dataset describing the estimated human presence (EHP) with two values – visitors and transiting persons. The temporal change of data is first analysed and further processed using two methodological approaches. First, the dasymetric method is used where the building geometry and technical attributes served as a target layer. Second, the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP. Resulting spatio-temporal patterns are compared to the census data. Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP. Potential use of proposed methodology within selected emergency situations is further discussed