Therese With Berge
Research Scientist
Biography
Authors
Marian Malte Weigel Therese With Berge Jukka Salonen Timo Lötjönen Bärbel Gerowitt Lars Olav BrandsæterAbstract
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Authors
Zahra Bitarafan Wiktoria Kaczmarek-Derda Therese With Berge Carl Emil Øyri Inger Sundheim FløistadAbstract
BACKGROUND As regulations on pesticides become more stringent, it is likely that there will be interest in steam as an alternative approach for soil disinfestation. This study investigates the feasibility of utilizing a soil steaming device for thermal control of invasive plants. RESULTS Seeds of Echinochloa crus-galli, Impatiens glandulifera, Solidago canadensis, and rhizome fragments of Reynoutria × bohemica were examined for thermal sensitivity through two exposure methods: (1) steam treatment of propagative material in soil; (2) exposure of propagative material to warm soil just after heated by steam. Soil temperatures in the range of 60–99 °C and dwelling period of 3 min were tested. Increased soil temperature decreased seed germination/rhizome sprouting. The exposure method had a significant effect where higher temperatures were needed to reduce the seed germination/rhizome sprouting in method 2 explained by the effect of extra heat given in method 1. Using method 1, for E. crus-galli and S. canadensis, the maximum mean temperature of approximately 80 °C was enough to achieve the effective weed control level (90%). This was lower for I. glandulifera and higher for R. × bohemica. Using method 2, 90% control was achieved at 95 °C for S. canadensis; more than 115 °C for I. glandulifera; and more than 130 °C for E. crus-galli and R. × bohemica. CONCLUSION Our findings showed a promising mortality rate for weeds propagative materials through soil steaming. However, the species showed varying responses to heat and therefore steam regulation should be based on the differences in weeds' susceptibility to heat.
Authors
Ingeborg Klingen Nils Bjugstad Therese With Berge Krzysztof Kusnierek Hans Wilhelm Wedel-Jarlsberg Roger Holten Anette Sundbye Lene Sigsgaard Håvard Eikemo Kirsten Tørresen Valborg KvakkestadAbstract
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Abstract
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Abstract
Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.
Abstract
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.
Authors
Björn Ringselle Benedikte Watne Oliver Therese With Berge Inger Sundheim Fløistad Liv Berge Lars Olav BrandsæterAbstract
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Authors
Ran Nisim Lati Jesper Rasmussen Dionisio Andújar Jose Dorado Therese With Berge Christina Wellhausen Michael Pflanz Henning Nordmeyer Michael Schirrmann Hanan Eizenberg Paul Neve Rasmus Nyholm Jørgensen Svend ChristensenAbstract
The adoption of site-specific weed management (SSWM) technologies by farmers is not aligned with the scientific achievements in this field. While scientists have demonstrated significant success in real-time weed identification, phenotyping and accurate weed mapping by using various sensors and platforms, the integration by farmers of SSWM and weed phenotyping tools into weed management protocols is limited. This gap was therefore a central topic of discussion at the most recent workshop of the SSWM Working Group arranged by the European Weed Research Society (EWRS). This insight paper aims to summarise the presentations and discussions of some of the workshop panels and to highlight different aspects of weed identification and spray application that were thought to hinder SSWM adoption. It also aims to share views and thoughts regarding steps that can be taken to facilitate future implementation of SSWM.
Abstract
© 2019 Elsevier Ltd. All rights reserved. Dette er den aksepterte versjonen av en artikkel publisert i Agricultural Systems. Du finner den publiserte artikkelen her: https://doi.org/10.1016/j.agsy.2019.102741 // This is the postprint version of the article published in Agricultural Systems. You can find the published article here: https://doi.org/10.1016/j.agsy.2019.102741
Abstract
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Authors
Björn Ringselle Therese With Berge Daniel Stout Tor Arvid Breland Paul E. Hatcher Espen Haugland Matthias Koesling Kjell Mangerud Tor Lunnan Lars Olav BrandsæterAbstract
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Authors
Trygve Utstumo Frode Urdal Anders Brevik Jarle Dørum Jan Netland Øyvind Overskeid Therese With Berge Jan Tommy GravdahlAbstract
© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 7.9.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract
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Authors
JC. Streibig J. Rasmussen D. Andùjar C. Andreasen Therese With Berge D. Chachalis T. Dittmann Gerhard Gerhardsen T. M. Giselsson P. Hamouz C. Jaeger-Hansen K. Jensen R.N. Jørgensen M. Keller M. Laursen H.S Midtiby J. Nielsen S. Muller H. Nordmeyer G. Peteinatos A Papadopoulos J. Svensgaard M. Weis S. ChristensenAbstract
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Abstract
Arable weeds are generally distributed in patches, while herbicides are applied uniformly. Herbicides can be saved if only the patches are sprayed, i.e. patch spraying (PS). Bottlenecks for cost-effective PS are weed monitoring technology and valid technology-based decision rules for PS (thresholds). The novel machine vision algorithm Weedcer has been developed as an efficient weed monitoring tool for PS. Weedcer estimates the proportions of young weed leaves and cereal leaves in high resolution red–green–blue images. We conducted field trials to test relative weed cover (RWC) and relative mayweed cover (RMC) estimated by Weedcer as decision rules for PS. RWC is the total weed cover divided by the total plant cover and RMC is the mayweed cover divided by the total plant cover. The main criterion for evaluation and basis of these thresholds was the measured grain yield. Images (about 0.06-m2) were acquired with a GPS guided autonomous field robot in spring, the normal time for spraying seed-propagated broadleaf weeds in both winter – and spring cereals in Norway. Three map-based trials (weed monitoring and spraying in two separate operations) showed that mean RWC per management unit (12.0 × 12.5-m) was generally adequate. In winter wheat heavily infested with scentless mayweed (Tripleurospermum inodorum (L.) Sch.Bip.) and/or scented mayweed (Matricaria recutita L.), the mean RMC per management unit was more adequate. Progress during the project allowed three additional trials conducted in real-time (weed monitoring and spraying in the same operation). These were conducted with the robot in spring cereals, and showed that a weighted moving average of RWC per image was adequate. The sprayed and unsprayed management units in these trials were minimum 3.0 × 3.0-m and 0.5 × 3.0-m, respectively. Results indicated that the Weedcer-based thresholds should be lower in wheat (Triticum aestivum) than in barley (Hordeum vulgare).
Abstract
Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm ("WeedFinder") estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red-green-blue images. The ability of "WeedFinder" to predict 'spray'/'no spray' decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, "WeedFinder" gave correct spray decisions in 65-85% of the test images. With discriminant analysis, corresponding mean rates were 84-90%. Future versions of "WeedFinder" must be more accurate and accommodate weed species recognition.
Abstract
A possible cost-effective real-time patch spraying implementation against seed-propagated broad-leaved weeds in cereals is a camera mounted in front of the tractor taking images at feasible distances in the direction of travel, on-board image analysis software and entire boom switched on and off. To assess this implementation, manual weed counts (0.25 m(2) quadrats) in a 1.5 m x 2 m grid, were used to simulate camera outputs. Each quadrat was classified into 'spray' and 'not spray' decisions based on a threshold model, and the resulting map defined the 'ground truth'. Subsequently, 'on/off' spraying at larger control areas where sizes were given by the boom width and image distance, and spraying decision controlled by weed status at the single quadrat simulating the camera's view, were simulated. These coarser maps were compared with 'ground truth', to estimate mapping error (area above threshold not sprayed), spraying error (area below threshold sprayed), total error (sum of mapping and spraying error) and the herbicide reduction. Three levels of the threshold model were tested. Results were used to fit models that predict errors from boom width and image distance. Size of control area did not on average affect the magnitude of the simulated herbicide reductions, but the bigger the control area the higher the risk that the simulated herbicide reduction deviate from the reduction in 'ground truth'. Mean simulated herbicide reductions were 42-59%, depending on threshold level. Only minor differences due to threshold level were seen for mean mapping and spraying errors at given spraying resolutions. Using original threshold level and image distance 2 m, predicted total errors for boom widths 2 m, 6 m, 20 m and 40 m would be 6%, 10%, 16% and 17%, respectively. Results indicate that control area should not exceed about 10 m 2 if acceptable total error is maximum 10%.
Authors
Zahra Bitarafan Wiktoria Kaczmarek-Derda Rafael De Andrade Moral Pierre-Adrien Rivier Therese With Berge Christian AndreasenAbstract
No abstract has been registered
Authors
Zahra Bitarafan Wiktoria Kaczmarek-Derda Therese With Berge Inger Sundheim Fløistad Christian AndreasenAbstract
No abstract has been registered
Authors
Marian Malte Weigel Therese With Berge Jukka Salonen Timo Lötjönen Bärbel Gerowitt Lars Olav BrandsæterAbstract
No abstract has been registered
