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    Publisher Correction: Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps
    (London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, Michael
    Correction to: Scientific Reports https://doi.org/10.1038/s41598-021-89930-w, published online 17 May 2021
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    Solar energy policy implementation in Ghana: A LEAP model analysis
    ([Amsterdam] : Elsevier B.V., 2022) Amo-Aidoo, A.; Kumi, E.N.; Hensel, O.; Korese, J.K.; Sturm, B.
    Current global climate change mitigation programs have been unable to meet the Paris Agreement's targets, and Ghana's situation is no exception. There is, therefore, an increased need for intensification of renewable energy deployment programs with an emphasis on solar energy as it constitutes about 90% of Ghana's installed renewable energy generation capacity. The study demonstrates how appropriate renewable energy policy can drive solar energy development in Ghana. Electricity demand scenarios were developed using historical data from 2000 to 2018, after which projections were made up to 2030 based on the average year-on-year electricity growth rate. Of the three electricity demand categories, residential demand experienced a steeper growth rate in comparison with the special load tariff, non-residential, and street lighting sectors. On the supply side, low, moderate, and visionary supply scenarios had increased solar penetration of 5 %, 10 %, and 15 % of the installed generation capacity respectively. While appreciable gains were made in the low and moderate supply scenarios, the visionary supply scenario could meet the renewable energy target with solar energy by 2030; leading to universal access to electricity while offsetting over 13 million metric tonnes of carbon dioxide in the process.
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    Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps
    (London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, Michael
    While insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role in increased acceptance and application of IPM in practice. In this study, we tested the differentiation of two whitefly species and their natural enemies trapped on yellow sticky traps (YSTs) via image processing approaches under practical conditions. Using the bag of visual words (BoVW) algorithm, accurate differentiation between both natural enemies and the Trialeurodes vaporariorum and Bemisia tabaci species was possible, whereas the procedure for B. tabaci could not be used to differentiate this species from T. vaporariorum. The decay of species was considered using fresh and aged catches of all the species on the YSTs, and different pooling scenarios were applied to enhance model performance. The best performance was reached when fresh and aged individuals were used together and the whitefly species were pooled into one category for model training. With an independent dataset consisting of photos from the YSTs that were placed in greenhouses and consequently with a naturally occurring species mixture as the background, a differentiation rate of more than 85% was reached for natural enemies and whiteflies.