2 research outputs found
Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne
platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle
at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is
developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental
results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at
farmland scales
Combined Effect of Porosity and Surface Chemistry on the Electrochemical Reduction of Oxygen on Cellular Vitreous Carbon Foam Catalyst
A new
mechanism of O<sub>2</sub> reduction, which follows principles
different from those generally accepted for describing ORR reduction
on heteroatom-doped carbons, is suggested. It is based on the ability
of oxygen to strongly adsorb in narrow hydrophobic pores. In this
respect, a cellular vitreous carbon foam–graphene oxide composite
was synthesized. The materials were doped with sulfur and nitrogen
and/or heat-treated at 950 °C in order to modify their surface
chemistry. The resultant samples presented a macro-/microporous nature
and were tested as ORR catalysts. To understand the reduction process,
their surfaces were extensively characterized from texture and chemistry
points of view. The treatment applied markedly changed the volumes
of small micropores and the surface hydrophilicity/polarity character.
The results showed that the electron transfer number was between 3.87
and 3.96 and the onset potential reached 0.879 V for the best-performing
sample. It is noteworthy that the best-performing sample has the highest
volume of pores smaller than 0.7 nm while there was no heteroatom
doping. The hydrophobicity and the strong adsorption forces provided
by these pores to pull oxygen inside are the possible reasons for
the observed excellent performance. A decrease in the volume of these
pores resulted in a decrease in the catalytic performance. When the
surface was modified with heteroatoms, the performances worsened further
because of the induced hydrophilicity