Weed problems in various tillage systems in the Nordic countries

There is an increasing use of various forms of reduced tillage or no-tillage in the Nordic countries. This will favour the growth of grass weed species and perennial species. Perennial creeping weeds like Elymus repens, Cirsium arvense, and Sonchus arvensis are important in all Nordic countries. Stationary weeds such as Taraxacum spp., Artemisia vulgaris and volunteer grassland species increase in areas with reduced tillage and especially no-tillage systems. Winter annual and biennial species such as Matricaria perforata, Poa annua, Alopecurus geniculatus and Stellaria media are frequently occuring weeds in reduced tillage systems in all countries, while Alopecurus myosuroides and Apera spica-venti are problems in Denmark and Southern parts of Sweden and Finland

Late-Autumn Ramet Sprouting of Three Arable Creeping Perennial Weed Species

Elymus repens (L.) Gould), Cirsium arvense (L.) Scop. and Sonchus arvensis L. are important arable creeping perennial weeds in Europe. These are clonal plants with subterranean reproductive organs (E. repens, rhizomes, the two dicots, horizontal creeping roots) sprouting from ramets. We tested the sprouting ability and early growth of ramet sprouts at temperatures typical for Nordic autumn climate and with different preconditions of the mother plant (time in autumn, mother plant age, climate change experiences of the mother plants (two experiments)). The species reacted differently, with S. arvensis not sprouting at all, and C. arvense ramets sprouting at higher temperatures than those of E. repens, which sprouted at all tested temperatures. Plant age affected only the ramet sprout biomass of E. repens. Climate change during mother plant growth only affected C. arvense, with the highest above-ground biomass of the sprouted ramets at an elevated temperature and ambient CO2. Testing earlier in autumn showed more sprouting and biomass for C. arvense and E. repens than testing later in the season. The observed temperature responses confirmed more and bigger sprouts with higher autumn temperatures. Controlling the sprouted ramets in autumn is easier for E. repens than for C. arvensis. Due to their low/no sprouting ability in autumn, the ramets of S. arvensis cannot be controlled in autumn.Late-Autumn Ramet Sprouting of Three Arable Creeping Perennial Weed SpeciespublishedVersio

Open process innovation practices : an exploratory study

Master's thesis Industrial Economics and Technology Management IND590 - University of Agder 2018Konfidensiell til / confidential until 01.07.202

Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning

With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person’s behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users’ privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5–2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human–robot interaction for various practical applications, such as mobile robots for eldercare.publishedVersio

Effects of integrated grassland renewal strategies on annual and perennial weeds in the sowing year and subsequent production years

Appropriate weed control measures during the renewal phase of temporary grasslands are critical to ensure high yields during the whole grassland lifecycle. The aim of this study was to determine which integrated grassland renewal strategy can most effectively control annual weeds in the sowing year and delay perennial weed re-establishment. Four split-plot trials were established at three sites dominated by Rumex spp. along a south-north gradient in Norway. The annual and perennial weed abundance was recorded during the sowing year and two or three production years. Main plots tested seven renewal strategies: 1. Spring plowing, 2. Spring plowing+companion crop (CC), 3. Summer cut+plowing, 4. Summer glyphosate+plowing, 5. Summer glyphosate+harrowing, 6. Late spring glyphosate+plowing, 7. Fall glyphosate+spring plowing+CC. Strategies 1–4 were tested in all four trials, strategy 5 in three trials, strategy 6 in two trials and strategy 7 in one trial. Plowing was performed at 20–25 cm depth, rotary harrowing at 15 cm depth, and glyphosate was applied at 2160 g a.i. ha-1. CC was spring barley (Hordeum vulgare). Subplots tested selective herbicide spraying (yes/no) in the sowing year. Results showed that effects of renewal strategies were often site-specific and differed between the sowing year and production years. Spring renewal resulted in higher perennial weed abundance than summer renewal in two out of four trials (by 3 and 12 percentage points, over all production years), and glyphosate followed by harrowing drastically increased Rumex spp. in one out of three trials (by 18 percentage points over all production years). CCs only significantly reduced perennial weed abundance in one trial (by 8 percentage points over all production years). In comparison, the selective herbicides had a strong effect on annual and perennial weeds in the sowing year in all trials. Selective herbicides reduced the weed cover from 32% to 7% cover, and averaged over the production years and sites, the perennial weed biomass fraction was 6 percentage points lower where herbicides had been applied. We conclude that while the tested renewal strategies provided variable and site-specific perennial weed control, selective herbicides were effective at controlling Rumex spp. and other perennial dicot weeds in the first two production years.publishedVersio

User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation

Kan bare brukes i forskningssammenheng, ikke kommersielt. Les mer her: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termsBuilding predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a system built specifically for each particular user will perform closer to the optimum. However, such a system would require more training data for every specific user, thus hindering its applicability for real-world scenarios. Collecting training data can be time consuming and expensive. For example, in clinical applications it can take weeks or months until enough data is collected to start training machine learning models. End users expect to start receiving quality feedback from a given system as soon as possible without having to rely on time consuming calibration and training procedures. In this work, we build and test user-adaptive models (UAM) which are predictive models that adapt to each users’ characteristics and behaviors with reduced training data. Our UAM are trained using deep transfer learning and data augmentation and were tested on two public datasets. The first one is an activity recognition dataset from accelerometer data. The second one is an emotion recognition dataset from speech recordings. Our results show that the UAM have a significant increase in recognition performance with reduced training data with respect to a general model. Furthermore, we show that individual characteristics such as gender can influence the models’ performance.acceptedVersio

Soil steaming to disinfect barnyardgrass-infested soil masses

Reusing soil can reduce environmental impacts associated with obtaining natural fresh soil during road construction and analogous activities. However, the movement and reuse of soils can spread numerous plant diseases and pests, including propagules of weeds and invasive alien plant species. To avoid the spread of barnyardgrass in reused soil, its seeds must be killed before that soil is spread to new areas. We investigated the possibility of thermal control of barnyardgrass seeds using a prototype of a stationary soil steaming device. One Polish and four Norwegian seed populations were examined for thermal sensitivity. To mimic a natural range in seed moisture content, dried seeds were moistened for 0, 12, 24, or 48 h before steaming. To find effective soil temperatures and whether exposure duration is important, we tested target soil temperatures in the range 60 to 99 C at an exposure duration of 90 s (Experiment 1) and exposure durations of 30, 90, or 180 s with a target temperature of 99 C (Experiment 2). In a third experiment, we tested exposure durations of 90, 180, and 540 s at 99 C (Experiment 3). Obtaining target temperatures was challenging. For target temperatures of 60, 70, 80, and 99 C, the actual temperatures obtained were 59 to 69, 74 to 76, 77 to 83, and 94 to 99 C, respectively. After steaming treatments, seed germination was followed for 28 d in a greenhouse. Maximum soil temperature affected seed germination, but exposure duration did not. Seed premoistening was of influence but varied among temperatures and populations. The relationships between maximum soil temperature and seed germination were described by a common dose–response function. Seed germination was reduced by 50% when the maximum soil temperature reached 62 to 68 C and 90% at 76 to 86 C. For total weed control, 94 C was required in four populations, whereas 79 C was sufficient in one Norwegian population.Soil steaming to disinfect barnyardgrass-infested soil massespublishedVersio

Monitoring In-Home Emergency Situation and Preserve Privacy using Multi-modal Sensing and Deep Learning

Videos and images are commonly used in home monitoring systems. However, detecting emergencies in-home while preserving privacy is a challenging task concerning Human Activity Recognition (HAR). In recent years, HAR combined with deep learning has drawn much attention from the general public. Besides that, relying entirely on a single sensor modal-ity is not promising. In this paper, depth images and radar presence data were used to investigate if such sensor data can tackle the challenge of a system's ability to detect abnormal and normal situations while preserving privacy. The recurrence plots and wavelet transformations were used to make a two-dimensional representation of the presence radar data. Moreover, we fused data from both sensors using data-level, feature-level, and decision-level fusions. The decision-level fusion showed its superiority over the other two techniques. For the decision-level fusion, a combination of the depth images and presence data recurrence plots trained first on convolutional neural networks (CNN). The output was fed into support vector machines, which yielded the best accuracy of 99.98%.acceptedVersio

Enabling Participants to Play Rhythmic Solos Within a Group via Auctions

The paper presents the interactive music system SoloJam, which allows a group of participants with little or no musical training to effectively play together in a ``band-like'' setting. It allows the participants to take turns playing solos made up of rhythmic pattern sequences. We specify the issue at hand for allowing such participation as being the requirement of decentralised coherent circulation of playing solos. This is to be realised by some form of intelligence within the devices used for participation. Here we take inspiration from the Economic Sciences, and propose this intelligence to take the form of making devices possessing the capability of evaluating their utility of playing the next solo, the capability of holding auctions, and of bidding within them. We show that holding auctions and bidding within them enables decentralisation of co-ordinating solo circulation, and a properly designed utility function enables coherence in the musical output. The approach helps achieve decentralised coherent circulation with artificial agents simulating human participants. The effectiveness of the approach is further supported when human users participate. As a result, the approach is shown to be effective at enabling participants with little or no musical training to play together in SoloJam

Bekjemping av grasugras med Axial i bladfaksfrøeng

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