Role of Drones in Characterizing Soil Water Content in Open Field Cultivation

Soil water content is a central topic in open field cultivation. In Finland’s boreal region with four thermal seasons, it has many roles which alter throughout the year. Climate change is changing the weather patterns, affecting all water-related processes and challenging the current farming practices. Better understanding of soils and their characteristics regarding response to water processes is called for, and data collection has a key role in this. Precision agriculture has been driving data intensification in farming. Unmanned aerial vehicles, or drones, have many applications and overall wide interest as an emerging technology in agriculture. Yet they lack an established role in day-to-day farming practices. Regarding data collection in open field cultivation, drones can be compared – or combined – with satellites, rovers, stationary devices, as well as plain old on-site observations by the farmer. In this study we give an overview of recent published literature, looking at data collection from the perspective of soil water information. We assess the opportunities and challenges of using drones in characterizing soil water content, mainly using soil and plant properties as proxies for it. Drones are useful in on-demand, nonintrusive, high-resolution spatial mapping of field properties. Soil moisture monitoring however requires frequent measurements, limiting the applicability of current drones.acceptedVersionPeer reviewe

Obtaining a ROS-Based Face Recognition and Object Detection : Hardware and Software Issues

This paper presents solutions for methodological issues that can occur when obtaining face recognition and object detection for a ROS-based (Robot Operating System) open-source platform. Ubuntu 18.04, ROS Melodic and Google TensorFlow 1.14 are used in programming the software environment. TurtleBot2 (Kobuki) mobile robot with additional onboard sensors are used to conduct the experiments. Entire system configurations and specific hardware modifications that were proved mandatory to make out the system functionality are also clarified. Coding (e.g., Python) and sensors installations are detailed both in onboard and remote laptop computers. In experiments, TensorFlow face recognition and object detection are examined by using the TurtleBot2 robot. Results show how objects and faces were detected when the robot is navigating in the previously 2D mapped indoor environment.acceptedVersionPeer reviewe

Mental Workload Assessment using Low-Channel Prefrontal EEG Signals

Objective: Monitoring stress using physiological signals has recently achieved a lot of attention since it has a significant adverse influence on an individual daily's health and efficiency. As it has been proven that stress and mental workload are proportionally correlated, several studies have proposed algorithms for stress monitoring by increasing the mental workload. Despite the promising results reported in the literature, a majority of the proposed algorithms require the employment of several physiological signals which hinder their real-life application. Nonetheless, the advent of low-cost wearable devices has provided a new possibility for outdoor stress monitoring. The objective of this paper is to present an algorithm for stress detection using low-channel prefrontal electroencephalography (EEG) data. Methods: Firstly, artifacts in EEG signals are removed. Secondly, EEG signals are split into sub-bands using the discrete wavelet transform and two nonlinear parameter-free features are extracted. Thirdly, the extracted features are fed to three classifiers, i.e., support vector machine, Adaboost, and the K-Nearest Neighbours to discriminate stress from relaxed states. Main results: According to the obtained results, the highest accuracy (80.24%) was achieved using the AdaBoost classifier. Significance:Given that the proposed method does not require any parameter adjustment before processing, it has the potential to be used in real-world scenarios.Peer reviewe

Classification of Masonry Bricks using Convolutional Neural Networks : a Case Study in a University-Industry Collaboration Project

This paper presents a case study – developing a computer-based classification framework to classify masonry bricks into three quality categories – carried out as a part of the Robocoast R&D Center project. The project aims at better collaboration between universities and industry by establishing an innovation platform where companies can bring their challenges to be addressed together with university experts. The project also promotes collaboration between universities being a part of the RoboAI Competence Centre – a joint research and innovation platform of Satakunta University of Applied Sciences (SAMK) and Tampere University, Pori unit. Automatic classification of bricks is important as it is foreseen that a robotic arm, powered by an automatic classifier, could replace the heavy and tedious work currently performed by humans in brick factories. A convolutional neural network-based solution, using a pretrained VGG-16 deep learning architecture, is proposed. Overall accuracy of 88 % was obtained when considering all three quality classes.When only discarding class 3 bricks, i.e., those that are not suitable for any construction work, the accuracy was 93 %.acceptedVersionPeer reviewe

On the inclusion of forest exposure pathways into a stylized lake-farm scenario in a geological repository safety analysis

Geological disposal of radioactive waste has been recognized as the ‘reference solution’ to ensure the safety required for the present and future society and environment. To study the possible exposure pathways from groundwater to humans, radioactive transport modelling is used. One of the ecosystems that may play a significant role when assessing the dose conversion factor (i.e. the dose resulting from a nominal release of 1 Bq/year of each radionuclide) for humans is forest. In this paper we have developed a model of a lake-farm system with a forest component. The biosphere system used in this study represents a typical agricultural scenario in Finland, amended with a typical forest. A lake is assumed to form due to post-glacial land uplift. The main features of this future lake have been obtained from our probabilistic shoreline displacement model. Both deterministic calculations and sensitivity analysis were carried out to simulate the model. The deterministic simulation demonstrates the behaviour of the studied radionuclides (36Cl, 135Cs, 129I, 237Np, 90Sr, 99Tc and 238U) and the proportions of different exposure pathways to humans. Particularly for 135Cs and 129I, forest pathways make a notable contribution to the dose conversion factor. The sensitivity analysis was done using two methods: EFAST and Sobol’. With both methods, the parameters related to the farm contribute the most to the variance of the dose conversion factor for humans. The study demonstrates that the exposure pathways related to forest products may make a considerable contribution to the dose conversion factor in a lake–farm–forest system. It is also confirmed that an advanced sensitivity analysis for a radionuclide transport and dose assessment model on such a landscape scale is feasible even with moderate computational efforts.publishedVersionPeer reviewe

Assessment of Crop Yield Prediction Capabilities of CNN Using Multisource Data

The growing abundance of digitally available spatial, geological, and climatological data opens up new opportunities for agricultural data-based input–output modeling. In our study, we took a convolutional neural network model previously developed on Unmanned Aerial Vehicle (UAV) image data only and set out to see whether additional inputs from multiple sources would improve the performance of the model. Using the model developed in a preceding study, we fed field-specific data from the following sources: near-infrared data from UAV overflights, Sentinel-2 multispectral data, weather data from locally installed Vantage Pro weather stations, topographical maps from National Land Survey of Finland, soil samplings, and soil conductivity data gathered with a Veris MSP3 soil conductivity probe. Either directly added or encoded as additional layers to the input data, we concluded that additional data helps the spatial point-in-time model learn better features, producing better fit models in the task of yield prediction. With data of four fields, the most significant performance improvements came from using all input data sources. We point out, however, that combining data of various spatial or temporal resolution (i.e., weather data, soil data, and weekly acquired images, for example) might cause data leakage between the training and testing data sets when training the CNNs and, therefore, the improvement rate of adding additional data layers should be interpreted with caution.acceptedVersionPeer reviewe

Assessment of Cloud Cover in Sentinel-2 Data Using Random Forest Classifier

In this paper, a novel cloud coverage assessment method for the Sentinel-2 data is presented. The method is based on the Random Forest classifier and the target values used in the training process are obtained by comparing the NDVI indexes calculated from the satellite and the UAV data. The developed method is shown to outperform the Sentinel Cloud Probability Mask (CLDPRB) and Scene Classification (SCL) data layers in detecting cloudy areas.acceptedVersionPeer reviewe

Historical Perspectives to Postglacial Uplift. Case Studies from the Lower Satakunta Region

This Brief discusses a unique mechanism to combine historical and archaeological evidence with statistical geodynamic modeling to study the historical development of the Eura region in lower Satakunta, Finland; this region is known for its rich cultural history. The book presents methods to model postglacial land uplift and the historical landscape. By using coupled data, it is possible to narrow the dating estimates of the archaeologically important places and structures and to build a more detailed reconstruction of landscape evolution in connection with the knowledge about human settlements and their movements. The resulting geospatial and uplift models are included as supplements.The primary audience for this book is experts and professionals working in the fields of archaeology, geography, geology and geospatial data analysis. </p

Using the nonlinear control of anaesthesia-induced hypersensitivity of EEG at burst suppression level to test the effects of radiofrequency radiation on brain function

Background In this study, investigating the effects of mobile phone radiation on test animals, eleven pigs were anaesthetised to the level where burst-suppression pattern appears in the electroencephalogram (EEG). At this level of anaesthesia both human subjects and animals show high sensitivity to external stimuli which produce EEG bursts during suppression. The burst-suppression phenomenon represents a nonlinear control system, where low-amplitude EEG abruptly switches to very high amplitude bursts. This switching can be triggered by very minor stimuli and the phenomenon has been described as hypersensitivity. To test if also radio frequency (RF) stimulation can trigger this nonlinear control, the animals were exposed to pulse modulated signal of a GSM mobile phone at 890 MHz. In the first phase of the experiment electromagnetic field (EMF) stimulation was randomly switched on and off and the relation between EEG bursts and EMF stimulation onsets and endpoints were studied. In the second phase a continuous RF stimulation at 31 W/kg was applied for 10 minutes. The ECG, the EEG, and the subcutaneous temperature were recorded. Results No correlation between the exposure and the EEG burst occurrences was observed in phase I measurements. No significant changes were observed in the EEG activity of the pigs during phase II measurements although several EEG signal analysis methods were applied. The temperature measured subcutaneously from the pigs' head increased by 1.6°C and the heart rate by 14.2 bpm on the average during the 10 min exposure periods. Conclusion The hypothesis that RF radiation would produce sensory stimulation of somatosensory, auditory or visual system or directly affect the brain so as to produce EEG bursts during suppression was not confirmed.BioMed Central Open acces

Digitaalne signaali- ja pilditöötlus : loengukonspekt

Kopeerimine ja printimine keelatudhttp://www.ester.ee/record=b2234022*es