Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Sammendrag
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.
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Amos Samkumar Rajan Premkumar Katja Hannele Karppinen Inger Martinussen Richard V. Espley Laura Elina JaakolaSammendrag
No abstract has been registered
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Vicente Guallart Michael Salka Daniel Ibañez Fabio Salbitano Silvano Fares Arne Sæbø Stefano Boeri Livia Shamir Lucrezia De Marco Sofia Paoli Maria Chiara Pastore Jerylee Wilkes-Allemann Evelyn Coleman Brantschen Ivana ŽivojinovićSammendrag
No abstract has been registered
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Arne Sæbø Hans Martin Hanslin Bart Muys David W. Shanafelt Tommaso Sitzia Roberto TognettiSammendrag
No abstract has been registered
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Silvano Fares Teodoro Georgiadis Arne Sæbø Ben Somers Koenraad Van Meerbeek Eva Beele Roberto Tognetti Giuseppe E. Scarascia-MugnozzaSammendrag
No abstract has been registered
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Monica Ubalde-Lopez Mark Nieuwenhuijsen Giuseppina Spano Giovanni Sanesi Carlo Calfapietra Alice Meyer-Grandbastien Liz O’Brien Giovanna Ottaviani Aalmo Fabio Salbitano Jerylee Wilkes-Allemann Payam DadvandSammendrag
No abstract has been registered
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No abstract has been registered
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No abstract has been registered
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No abstract has been registered
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Lampros LamprinakisSammendrag
No abstract has been registered