Flowers, leaves or both? How to obtain suitable images for automated plant identification

Background: Deep learning algorithms for automated plant identification need large quantities of precisely labelled images in order to produce reliable classification results. Here, we explore what kind of perspectives and their combinations contain more characteristic information and therefore allow for higher identification accuracy. Results: We developed an image-capturing scheme to create observations of flowering plants. Each observation comprises five in-situ images of the same individual from predefined perspectives (entire plant, flower frontal- and lateral view, leaf top- and back side view). We collected a completely balanced dataset comprising 100 observations for each of 101 species with an emphasis on groups of conspecific and visually similar species including twelve Poaceae species. We used this dataset to train convolutional neural networks and determine the prediction accuracy for each single perspective and their combinations via score level fusion. Top-1 accuracies ranged between 77% (entire plant) and 97% (fusion of all perspectives) when averaged across species. Flower frontal view achieved the highest accuracy (88%). Fusing flower frontal, flower lateral and leaf top views yields the most reasonable compromise with respect to acquisition effort and accuracy (96%). The perspective achieving the highest accuracy was species dependent. Conclusions: We argue that image databases of herbaceous plants would benefit from multi organ observations, comprising at least the front and lateral perspective of flowers and the leaf top view

Ecological study of aquatic midges and some related insects with special reference to feeding habits

Die Schweiz ist ein reiches Land. Sie verfügt über viele Millionäre. Der große Reichtum konzentriert sich auf wenige Familien und Personen. In der Schweiz leben aber auch eine halbe Million der Bevölkerung (7,5 Mio.) in Haushalten von Erwerbstätigen, die weniger als das Existenzminimum verdienen. Über 200‘000 Personen sind auf Sozialhilfe angewiesen. Bei den Vermögen und den verfügbaren Einkommen hat sich in den letzten Jahren die Kluft zwischen den obersten und untersten zehn Prozent verschärft. Die Zunahme der sozialen Ungleichheit erhöht die soziale Brisanz, was mehr zu ergründen ist. Die soziale Differenzierung dokumentiert Prozesse der Globalisierung. Sie reproduziert und spezifiziert alte soziale Ungleichheiten. Wichtig ist, dass die Soziale Arbeit das thematisiert und weiter theoretisiert

Genetic variation for nutrient use efficiency in maize under different tillage and fertilization regimes with special emphasis to plant microbe interaction

Conservation tillage (no-till and reduced tillage) brings many benefits with respect to soil fertility and energy use, but it also has drawbacks regarding the need for synthetic fertilizers and herbicides. To promote conversation tillage in organic farming systems, crop rotation, fertilization and weed control have to be optimized. In addition, crop varieties are needed with improved nutrient use efficiency (NUE) and high weed competitiveness or tolerance

Electronic structure of intentionally disordered AlAs/GaAs superlattices

We use realistic pseudopotentials and a plane-wave basis to study the electronic structure of non-periodic, three-dimensional, 2000-atom (AlAs)_n/(GaAs)_m (001) superlattices, where the individual layer thicknesses n,m = {1,2,3} are randomly selected. We find that while the band gap of the equivalent (n = m = 2) ordered superlattice is indirect, random fluctuations in layer thicknesses lead to a direct gap in the planar Brillouin zone, strong wavefunction localization along the growth direction, short radiative lifetimes, and a significant band-gap reduction, in agreement with experiments on such intentionally grown disordered superlattices.Comment: 10 pages, REVTeX and EPSF macros, 4 figures in postscript. e-mail to [email protected]

Smart cellulose fibers coated with carbon nanotube networks

Smart multi-walled carbon nanotube (MWCNT)-coated cellulose fibers with a unique sensing ability were manufactured by a simple dip coating process. The formation of electrically-conducting MWCNT networks on cellulose mono- and multi-filament fiber surfaces was confirmed by electrical resistance measurements and visualized by scanning electron microscopy. The interaction between MWCNT networks and cellulose fiber was investigated by Raman spectroscopy. The piezoresistivity of these fibers for strain sensing was investigated. The MWCNT-coated cellulose fibers exhibited a unique linear strain-dependent electrical resistance change up to 18% strain, with good reversibility and repeatability. In addition, the sensing behavior of these fibers to volatile molecules (including vapors of methanol, ethanol, acetone, chloroform and tetrahydrofuran) was investigated. The results revealed a rapid response, high sensitivity and good reproducibility for these chemical vapors. Besides, they showed good selectivity to different vapors. It is suggested that the intrinsic physical and chemical features of cellulose fiber, well-formed MWCNT networks and favorable MWCNT-cellulose interaction caused the unique and excellent sensing ability of the MWCNT-coated cellulose fibers, which have the potential to be used as smart materials

Carbon sequestration and stabilization in a 40-year agronomic long-term experiment

Soils contain more carbon (C) in the form of organic matter (soil organic matter = SOM) than the entire atmosphere and global vegetation put together. They are thus a central component of the global C cycle and its largest dynamic reservoir. On the one hand, intelligent agricultural practices are discussed as a way of mitigating climate change because they can increase the amount of SOM and thus actively remove C from the atmosphere. On the other hand, all intensively used soils lose C in the long term. Central questions in this context revolve around the extent and dynamics of storage, the stabilisation mechanisms involved and the impact of agricultural use on the C budget

Deep learning in plant phenological research: A systematic literature review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field

Agronomical techniques to improve technological and sanitary quality

In spite of variable grain protein contents, baking quality of organic wheat was found to be acceptable to good. Mycotoxin (DON) infestation was generally low on tested grain samples. Choice of wheat cultivar was the most efficient way to obtain higher grain quality. Fertilization with readily available nitrogen and, to a lower extent, association with legumes and green manures with mixtures containing fodder legumes also improved grain quality. Reduced tillage affected soil quality and wheat yield but had little effects on grain quality

Techniques to improve technological and sanitary quality

The demand for high quality organic bread cereals is increasing. In spite of variable grain protein contents, baking quality of organic wheat was found to be acceptable to good. Mycotoxin (DON) contents were generally low on tested grain samples. Choice of the wheat cultivar is the most efficient way to obtain higher grain quality. Fertilization with readily available nitrogen and, to a lower extent, association with legumes and green manuring with mixtures containing fodder legumes can also improve grain quality. Reduced tillage affects soil quality and wheat yield but has little effects on grain quality