ORCID Profile
0000-0003-3692-382X
Current Organisations
Office of National Intelligence
,
University of Southern Queensland
,
University of Southern Queensland - Springfield Campus
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Publisher: Radiological Society of North America (RSNA)
Date: 04-2020
Publisher: Wiley
Date: 10-07-2007
Publisher: MDPI AG
Date: 25-02-2022
DOI: 10.3390/RS14051136
Abstract: Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports regionally and globally to meet human food security. This paper incorporates remote satellite-based information in a wheat-growing region in South Australia to estimate the yield by integrating the kernel ridge regression (KRR) method coupled with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the grey wolf optimisation (GWO). The hybrid model, ‘GWO-CEEMDAN-KRR,’ employing an initial pool of 23 different satellite-based predictors, is seen to outperform all the benchmark models and all the feature selection (ant colony, atom search, and particle swarm optimisation) methods that are implemented using a set of carefully screened satellite variables and a feature decomposition or CEEMDAN approach. A suite of statistical metrics and infographics comparing the predicted and measured yield shows a model prediction error that can be reduced by ~20% by employing the proposed GWO-CEEMDAN-KRR model. With the metrics verifying the accuracy of simulations, we also show that it is possible to optimise the wheat yield to achieve agricultural profits by quantifying and including the effects of satellite variables on potential yield. With further improvements in the proposed methodology, the GWO-CEEMDAN-KRR model can be adopted in agricultural yield simulation that requires remote sensing data to establish the relationships between crop health, yield, and other productivity features to support precision agriculture.
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 04-2023
Publisher: MDPI AG
Date: 08-02-2022
DOI: 10.3390/RS14030805
Abstract: Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries.
Publisher: McMaster University Library
Date: 18-10-2022
Publisher: MDPI AG
Date: 18-05-2023
DOI: 10.20944/PREPRINTS202305.1323.V1
Abstract: Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved & % classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.
Publisher: Elsevier BV
Date: 05-2007
DOI: 10.1016/J.BBAMEM.2007.02.006
Abstract: Tethered bilayer lipid membranes (tBLMs) are robust and flexible model platforms for the investigation of various membrane related processes. They are especially suited to study the incorporation and function of ion channel proteins, where a high background resistance of the membrane is essential. Synthetic M2 peptides, analogues of the transmembrane fragment of the acetylcholine receptor, could be incorporated into two different membrane architectures. The functional reconstitution and the formation of a conducting pore are shown by electrochemical impedance spectroscopy (EIS). The pore is selective for small monovalent cations, while bulky ions cannot pass. This is a significant step towards a novel biosensing approach. We envision a device, where a stable and insulating membrane would be attached to an electronic read-out unit and embedded proteins would serve as actual sensing units.
Publisher: Elsevier BV
Date: 2024
Publisher: Elsevier BV
Date: 10-2022
Publisher: Elsevier BV
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 03-2020
DOI: 10.1016/J.SCITOTENV.2019.135934
Abstract: Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high dimensional nature of air quality predictor variables. The novelty of this research is to propose a hybrid early-warning artificial intelligence (AI) framework that can emulate hourly air quality variables (i.e., Particulate Matter 2.5, PM
Location: Switzerland
Location: Australia
No related grants have been discovered for Ekta Sharma.