Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Investigations into hyperspectral and high-resolution imaging of layered biosamples

The field of diagnostic imaging has experienced a rapid growth in recent years driven by the increased demand for better diagnosis and the desire to understand biological mechanisms at the microscopic level. It is predicted that the future diagnostic imaging techniques would require accurate diagnosis for well-defined predictions and classifications. Among the diagnostic imaging techniques, those using the optical wavelength regime are more preferred because of its non-invasive and non-ionizing properties that can reduce the exposure to harmful radiations. However, there are numerous challenges to overcome before optical imaging techniques can be used effectively in the automated diagnostic assessment of biological tissues, especially in thick and layered tissues. Processes such as high absorption, strong scattering, specular reflections, and autofluorescence makes the optical imaging of such samples tedious. For thick absorptive biological samples, the multiple scattering and absorption events occurring during the photon propagation, limit the penetration depth to the optical diffusion limit and the possibility to perform direct imaging in thicker samples. On the other hand, the high transmission and specular nature associated with transparent biosamples make it difficult for them to be imaged using light in reflection configuration. Thus, the current scenario requires specialised systems and trained personnel to image specific biosamples; absorptive or transparent. The operator skill dependence of the current techniques will increase the chance of diagnostic errors, which affect the accurate diagnosis of the disease state and can be a serious threat to survival. In brief, the currently available technologies for diagnostic imaging of such samples are limited by, (i) low working distance and small field of view for high-resolution measurements (ii) lack of chemical specificity, (iii) longer image acquisition time, (iv) high operator skill dependence leading to lack of automation possibilities, (v) destructive nature (require labelling or clearing), and (vi) lower imaging depth due to high absorption and scattering. Hence, there is a genuine need for novel optical imaging concepts and systems to be developed for the high-resolution imaging of both absorptive and transparent multilayered biosamples that can subdue these limitations. This research explores theoretical and experimental investigations on the various processes to mitigate the current limitations as mentioned earlier while imaging such biosamples. Based on the conducted literature survey, two multi-layered biosamples, namely the cornea of the eye (optically transparent) and plant leaf (optically absorptive), have been identified, whose optical imaging still faces challenges discussed earlier. The systems developed in this research are focused on overcoming the challenges associated with the diagnostic imaging of these samples. In this context, one of the major focuses of this research is to investigate the potential of two primary imaging modalities: spectral imaging and high-resolution imaging using structured illumination embedded with speckles, individually or by using a bimodal approach for sample characterisation and related diagnosis. Previous research has shown that the unique spectral signatures associated with the biosample can play a significant role in the accurate assessment of the underlying conditions of the sample. Thus, the inclusion of spectral information in high-resolution imaging techniques can ultimately lead to accurate diagnosis in layered biosamples. The realisation of such experimental systems that records the spectral characteristics along with high-resolution spatial information can prepare diagnostic imaging tools for facing the future digital world. In this context, the first objective of this research is to develop a non-contact, large-area surface hyperspectral imaging technique offering high spectral resolution. The research aims at designing the optics (including lens parameters and illumination schemes for large-area monitoring) and developing the data processing algorithms. An automated spectral imaging system to continuously monitor the small changes in the biosamples was developed. The developed configuration can acquire hyperspectral images with a spatial resolution of ~140 µm at a working distance of ~1 m and spectral resolution of ~1.4 nm. Also, the system can process the data at high speeds using the spectral index approach (7 GB size data was processed in less than a minute on a i7 processor running at 1.9 GHz speed with 32 GB RAM) and is therefore envisaged to reduce the processing time in monitoring large areas (such as vertical hydroponic farms spanning an area of the order of ~15 m2 - 20 m2 in a single scan). These systems also helped in reducing the dependence on human labour and skills in decision making. Hence, this system can also help in the automated and accurate characterisation or disease diagnosis in the samples using appropriate spectral libraries developed during the research. Investigating the processes and mechanisms occurring in a stressed and healthy biosample is crucial. This leads to the second main objective of this research as the development of a high-resolution imaging system to image multilayered biosamples with high lateral and axial resolution with long working distances enabling non-contact and non-invasive imaging. The light propagation in layered biological tissues is investigated using Monte Carlo simulations considering the specific cases for both optically transparent and absorptive regions using unstructured (conventional) and structured laser light. The simulation results illustrated that the use of structured light for imaging such thick multilayered samples proved to be better for diagnostic imaging. This approach was followed for the realization of an imaging system employing 400 structured illumination patterns embedded with speckles (named as embedded speckle structured illumination microscope or in short as ES-SIM). The conceptualized and developed imaging configuration can image the biosamples with a lateral resolution of 1 µm (in the present case, this was limited by pixel size of the camera used) and an axial resolution (Δz) of ~3 µm, at long working distances (greater than 1 cm) and field of view (FOV) of 0.5 mm × 0.5 mm using a 20× objective lens (0.45 NA, 19 mm WD). High axial and lateral resolutions offered by this technique allow for accurate 3D reconstruction enabling the understanding of various morphological changes that can occur in the biosample as the disease progresses. The depth of imaging achieved for absorptive sample is ~ 80 µm and for transparent sample is ~ 900 µm. The capability of the developed high-resolution imaging configuration for both absorptive and transparent biosamples with multiple layers for diagnostic applications such as necrosis detection and corneal characterisation were demonstrated with test samples. Further to optimise the image acquisition time and reduce imaging artefacts, a computational imaging algorithm was developed (termed as FAST ES-SIM) which reduced the image acquisition time by 10 times and improved the speckle contrast by ~5 times. The final objective of this thesis is to conceptualise and develop a bimodal imaging system integrating the spectroscopic and high-resolution imaging of layered biosamples for diagnostic applications along the wavelength range from 400 nm to 1000 nm. Two microscopic configurations namely, structured illumination based hyperspectral microscope (SIHM) and speckle based hyperspectral microscope (SHSM) integrating the high resolution imaging and spectral imaging aspects were conceptualised and fabricated. The developed SIHM configuration offers a spatial resolution of ~586 nm (lateral) and ~5 µm (axial) at the wavelength of 640 nm using an objective lens (50×, 0.55 NA, 13 mm WD, Apochromat). Structured illumination patterns enhanced the lateral resolution of the system beyond the theoretical diffraction limit of 710 nm (at 0.55 NA and λ = 640 nm) and thereby improving the effective NA of the system by 1.2 times. The system also offers a high spectral resolution of ~1.4 nm. As an application, the structural and spectral changes occurring during the necrosis and chlorosis processes in a leaf sample was investigated using SIHM configuration. The developd SIHM used pushbroom configuration for spectral scanning which demanded more acquisition time for each pattern projection (for 9 structured illumination patterns, approx. 4 min). To reduce the acquisition time, a single scan hyperspectral microscope is developed termed as SHSM using the wavelength dependent nature of laser speckle generated using a static diffuser. This configuration reduced the image acquisition time by ~4.5 times. The identified research problems that are targeted to be improved during this research included: Non-invasive corneal layer characterisation for corneal quality assessment of the eye and crop monitoring with associated disease diagnosis in hydroponics, specifically focusing on diseases such as necrosis and chlorosis. It is envisioned that the contributions from this research can help in improving diagnostic imaging of multi-layered biosamples, especially for applications in (i) biomedical area such as corneal characterisation and disease detection, and (ii) for comprehensive in-situ crop monitoring of plants. The developed systems are anticipated to drive a paradigm shift not only in the biomedical and agricultural industries but also in other sectors such as automotive, marine, and semiconductor industries in the future.Doctor of Philosoph

Single-shot LIBS: a rapid method for in situ and precise nutritional evaluation of hydroponic lettuce

Hydroponic farming has emerged as a promising method that can enable year around crop production, particularly in regions with non-arable land. Ensuring precise control over nutrient levels and growing conditions is imperative for optimizing crop quality and nutritional value. However, the existing state-of-the-art nutrient assessment methods demand tedious sample preparation and often prove to be either destructive or offline, lacking in options for in situ monitoring. Previous approaches to nutritional evaluation using laser-induced breakdown spectroscopy (LIBS) utilized multiple laser shots or labor-intensive sample preparation to achieve enhanced sensitivity. In this context, we propose a single-shot LIBS system with a custom-made optical collection unit coupled to spectrograph to improve sensitivity and reduce sample damage by employing low excitation energy levels (~ 1.5 mJ). This study demonstrates in situ nutrient monitoring of hydroponically grown lettuce leaves and roots using single-shot LIBS analysis, paving the way for enhanced crop cultivation practices and improved agricultural productivity. Additionally, we discuss energy optimization strategies aimed at improving sensitivity and achieving a high signal-to-background ratio, which are essential for effective and safe nutrient monitoring and analysis in hydroponic farming systems. The results and analysis reveal that highly reproducible and sensitive LIBS spectra can be obtained directly from lettuce plants without any prior sample preparation.Economic Development Board (EDB)Nanyang Technological UniversityNational Research Foundation (NRF)Singapore Food AgencyPublished versionThis research is supported by the National Research Foundation, Singapore and Singapore Food Agency, under its Singapore Food Story R&D Programme (Theme 1: Sustainable Urban Food Production) Grant Call (SFS_RND_ SUFP_001_03). This work was also supported by (i) a research collaboration agreement by Panasonic Factory Solutions Asia Pacifc (PFSAP) and School of Mechanical and Aerospace Engineering, NTU (RCA-80368) and (ii) COLE-EDB funding at COLE, NTU Singapore

Early and accurate nutrient deficiency detection in hydroponic crops using ensemble machine learning and hyperspectral imaging

Vertical indoor hydroponic farms are growing as a technological solution fostering agriculture productivity to address the ever increasing food demands in sustainable cities. These farms provide extensive control over the growing conditions to ensure all-weather cultivation of diverse crops within the available limited space. However, to assure the quality of hydroponic crops, continuous close-range crop monitoring and early detection of deficiencies are essential. Sensitive techniques such as hyperspectral imaging combined with ensemble based machine learning techniques have proven to provide improved reliable results. However, despite their potential, the application of these methods for early-stage nutrient deficiency detection in crops remains relatively underexplored. In this context, this research presents and proposes different machine learning-based approaches that utilizes ensemble techniques such as Random Forest (RF), Bagging or Bootstrap Aggregating, Adaboost or Adaptive Boosting, and eXtreme Gradient Boosting (XGB) classifiers for early detection of nutrient deficiencies in hydroponic crops. In the proposed approach, the features extracted from hyperspectral datacubes are trained to create machine learning models. Among the investigated models, the XGB classifier demonstrated the fastest computational time and test accuracy of 18.07 s and 99.6 %, respectively. This research also proposes a novel computer vision (CV) based approach to improve the tedious manual data labelling process involved in HSI dataset creation. Envisioned as an invaluable tool, the proposed non-invasive imaging system could detect as early as 3 days after stress induction and revolutionize the automated monitoring of indoor hydroponic farms with enhanced accuracy for a sustainable future.Ministry of Education (MOE)Nanyang Technological UniversityNational Research Foundation (NRF)Singapore Food AgencyPublished versionThis research is supported by the National Research Foundation, Singapore and Singapore Food Agency, under its Singapore Food Story R&D Programme (Theme 1: Sustainable Urban Food Production) Grant Call (SFS_RND_SUFP_001_03). This research was also supported by a research collaboration agreement by Panasonic Factory Solutions Asia Pacific (PFSAP) and School of Mechanical and Aerospace Engineering, NTU (RCA-80368). The authors also acknowledge financial support received through COLE-EDB funding at COLE, NTU and Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 Grant RG79/24

Hyperspectral z-scan: measurement of spectrally resolved nonlinear optical properties

Broadband hyperspectral z-scan using a supercontinuum light source is a convenient technique to obtain spectrally resolved nonlinear optical properties of the materials under investigation. Post-processing and segregation of the data obtained from the supercontinuum based hyperspectral z-scan measurement aids in determining the nonlinear optical properties with high spectral resolution. However, few data models exist to store and represent the large amount of information acquired from the hyperspectral z-scan measurement. In this paper, a 3D data model for representing the data obtained from broadband z-scan measurements and analysis is presented. This method would help in the quick characterization of spectrally resolved nonlinear optical properties of materials from a single z-scan measurement. The proposed model is used for obtaining the spectrally resolved nonlinear optical properties of rhodamine 6G.Nanyang Technological UniversitySubmitted/Accepted versionThe authors acknowledge NTU-India Connect programme and COLE-EDB, NTU for funding and research manpower support

Hyperspectral vision beyond 3D: a review

Hyperspectral imaging (HSI) technique has evolved dramatically over the past few decades, demonstrating its capability for a wide range of applications. Combining HSI with other advanced imaging modalities have resulted in versatile systems with exceptional imaging and spectroscopic capabilities. HSI originated as an imaging technique for assessing the spatial and spectral attributes of the objects under inspection. Attributes extracted from HSI can be well represented and visualised in 3-dimensional (3D) space using virtual reality (VR) and augmented reality (AR). Later advancements led to the development of 4D HSI cameras that can track the spatial and spectral characteristics of objects over time and became a hot topic in precision agriculture, medical imaging, and optical sorting. Recently, 5D HSI systems utilizing structured light were developed for measuring the time-dependent spectral characteristics along the entire shape of macroscopic objects under investigation, providing excellent spectral, spatial, and axial resolutions. Latest compact HSI cameras with panoramic imaging capabilities offering high spatial and spectral resolutions are perfect candidates for remote sensing applications. Such capabilities facilitated the evolution of HSI into machine vision, resulting in the creation of a new realm named hyperspectral vision that enables intelligent automation. This review highlights the technological advancements in the field of HSI, focusing on advanced imaging modalities to realise spectral vision beyond 3D for applications in machine vision and Industry 4.0. The review also throws light on the challenges faced during real-life implementations of such technologies and possible mitigation strategies. Approaches for enhancing the HSI data using matured technologies such as AR and VR are also discussed.Economic Development Board (EDB)National Research Foundation (NRF)Singapore Food AgencyThis research is supported by the National Research Foundation, Singapore and Singapore Food Agency, under its Singapore Food Story R&D Programme (Theme 1: Sustainable Urban Food Production) Grant Call (SFS_RND_SUFP_001_03). The authors also acknowledge financial support received through COLE-EDB funding at COLE, NTU