Publikasjoner
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2023
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Forfattere
Volkmar Timmermann Isabella Børja Nicholas Clarke Jostein Gohli Ari Hietala Jane Uhd Jepsen Paal Krokene Harald Haga Lislegård Nina Elisabeth Nagy Hans Nyeggen Sverre Solberg Halvor Solheim Thomas Solvin Arvid Svensson Mari Mette Tollefsrud Ole Petter Laksforsmo Vindstad Bjørn Økland Wenche AasSammendrag
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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.
Sammendrag
Material exiting the harvester is composed of chaff and straw. Chaff is a by-product of grain harvest comprises weed seeds and husk. Harvest Weed Seed Control (HWSC) systems aim at collecting and/or killing weed seeds in the chaff fraction during crop grain harvest. If chaff is removed or processed via impact mills or concentrated in a narrow zone in the field and collected, the overall weed infestation may be reduced in the following years. Chaff may be used as a new biomass feedstock, for example, as a renewable energy source, material for construction ( e.g. , insulating boards, cardboard, bedding), soil improvement ( e.g ., mulch, mushroom compost), and for agricultural purposes ( e.g. , weed growth inhibitor, animal diet). Using chaff directly is unfavorable because of its low bulk density. Therefore, compressing chaff into pellets can improve its handling. In this preliminary study, we assessed how pelletizing would affect the germinability of weed seeds in the chaff pellets. Whole wheat chaff and fine wheat chaff sieved were mixed with seeds of the two weed species scentless mayweed ( Tripleurospermum inodorum (L.) Sch.Bip.) and cornflower ( Centaurea cyanus L.), respectively. While 22% of T. inodorum seeds and 59% of C. cyanus seeds in wheat chaff samples were able to germinate, no weed seeds germinated from moist pelletized original and fine wheat chaff samples. The study indicates a low risk of spreading weed seeds with pelletized chaff probably because the heating during the pelletizing process kills the weed seeds.
Sammendrag
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Sammendrag
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Forfattere
Susanne ZazzeraSammendrag
MAMN-BIO MAMN-HAVSJ
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Forfattere
Bjørn Arild Hatteland Rafael De Andrade Moral Gunnhild Jaastad Gaute Myren Endre Bjotveit Irén Lunde Sekse Karin Westrum Nina TrandemSammendrag
BACKGROUND Integrated pest management (IPM) has a long history in fruit production and has become even more important with the implementation of the EU directive 2009/128/EC making IPM mandatory. In this study, we surveyed 30 apple orchards in Norway for 3 years (2016–2018) monitoring pest- and beneficial arthropods as well as evaluating fruit damage. We obtained growers’ diaries of pest management and used these data to study positive and negative correlations of pesticides with the different arthropod groups and damage due to pests. RESULTS IPM level had no significant effects on damage of harvested apples by arthropod pests. Furthermore, damage by arthropods was mainly caused by lepidopteran larvae, tortricids being especially important. The number of insecticide applications varied between 0 and 3 per year (mean 0.8), while acaricide applications varied between 0 and 1 per year (mean 0.06). Applications were often based on forecasts of important pest species such as the apple fruit moth (Argyresthia conjugella). Narrow-spectrum insecticides were commonly used against aphids and lepidopteran larvae, although broad-spectrum neonicotinoid (thiacloprid) insecticides were also applied. Anthocorid bugs and phytoseiid mites were the most abundant natural enemies in the studied orchards. However, we found large differences in abundance of various “beneficials” (e.g., lacewings, anthocorids, parasitic wasps) between eastern and western Norway. A low level of IPM negatively affected the abundance of spiders. CONCLUSION Lepidoptera was found to be the most important pest group in apple orchards. Insecticide use was overall low, but number of spray applications and use of broad-spectrum insecticides varied between growers and regions. IPM level did not predict the level of fruit damage by insects nor the abundance of important pests or most beneficial groups in an apple orchard. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.