2 research outputs found
Stochastic Perturbations on Low-Rank Hyperspectral Data for Image Classification
Hyperspectral imagery (HSI) contains hundreds of narrow contiguous bands of spectral signals. These signals, which form spectral signatures, provide a wealth of information that can be used to characterize material substances. In recent years machine learning has been used extensively to classify HSI data. While many excellent HSI classifiers have been proposed and deployed, the focus has been more on the design of the algorithms. This paper presents a novel data preprocessing method (LRSP) to improve classification accuracy by applying stochastic perturbations to the low-rank constituent of the dataset. The proposed architecture is composed of a low-rank and sparse decomposition, a degradation function and a constraint least squares filter. Experimental results confirm that popular state-of-the-art HSI classifiers can produce better classification results if supplied by LRSP-altered datasets rather than the original HSI datasets.
Predicting plant environmental exposure using remote sensing
Wheat is one of the most important crops globally with 776.4 million tonnes produced in
2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici
Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend
£0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A
preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase
which makes it difficult to detect before symptoms develop, after which point fungicide
intervention is ineffective.
In the second chapter of my thesis I use hyperspectral sensing and imaging techniques,
analysed with machine learning to detect and predict symptomatic Z. tritici infection in
winter wheat, in UK based field trials, with high accuracy. This has the potential to
improve detection and monitoring of symptomatic Z. tritici infection and could facilitate
precision agriculture methods, to use in the subsequent growing season, that optimise
fungicide use and increase yield.
In the third chapter of my thesis, I develop a multispectral imaging system which can detect
and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the
nitrogen source applied. Currently, plants are treated with nitrogen sources to increase
growth and yield, the most common being calcium ammonium nitrate. However, some
nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive
manufacture and ammonium sulphate in the cultivation and extraction of the narcotic
cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing,
multispectral imaging, and machine learning image analysis can be used to visualise and
differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis
of leaves from plants exposed to different nitrogen sources reveals shifts in colourful
metabolites that may contribute to altered reflectance signatures. This suggests that
different nitrogen feeding regimes alter plant secondary metabolism leading to changes in
plant leaf reflectance detectable via machine learning of multispectral data but not the
naked eye. These results could facilitate the development of technologies to monitor illegal
activities involving various nitrogen sources and further inform nitrogen application
requirements in agriculture.
In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral
imaging and machine learning image analysis developed in the third chapter to detect
asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field
trials, with high accuracy. This has the potential to improve detection and monitoring of all
stages of Z. tritici infection and could facilitate precision agriculture methods to be used
during the current growing season that optimise fungicide use and increase yield.Open Acces