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Hill forest vegetation cover in the southeastern part of Bangladesh has been facing degradation and depletion over a few decades. The forest estimation and mapping of this country are well documented and mainly restricted to the mangrove extent. However, the monitoring of the hill forest vegetation of Bangladesh is limited till now. This study monitor and analyzes the forest vegetation cover changes using Landsat imagery from 1974 to 2020, specifically in Khagrachari and Rangamati hill district, Bangladesh. We preprocess the satellite imagery and then perform a decision tree classification algorithm based on the spectral indexes derived from the imagery. The initial assessment indicates the negative change of dense vegetation/forest vegetation cover (FVC) in most parts of the study area since 1974. Further results show that ~57.17%, ~39.3%, ~31.27%, and ~24.97% of the total area were classified as FVC type in 1989,1999, 2010 and 2020, respectively. Besides, this study briefly discusses how the change of FVC impacts the life of the local indigenous community living around the area. This preliminary investigation highlights deforestation over the ~46 years around the study area, which could be beneficial for planning to manage and conserve forest resources, and protect the local indigenous community. [ABSTRACT FROM AUTHOR]

Potato (Solanum tuberosum L.) is one of the most significant vegetable crops grown globally, especially in developing countries. Over the last few years, global potato production has been increasing. This growth has created many opportunities for developing a wide range of value-added products from these crops. However, this requires monitoring the quality components of the tubers, such as moisture content, starch content, and soluble solid content. In particular, moisture content is one of the key quality parameters important for ensuring quality control throughout the supply chain and processing for consumer consumption. Ideally, moisture content would be estimated at the field level; however, current methods used by the industry to assess moisture content are time-consuming, labor-intensive, and destructive. Hence, the purpose of this study is to investigate the feasibility of hyperspectral imaging to quantify the moisture content of unpeeled potatoes before they were subsequently stored and processed. Hyperspectral images are collected from 47 intact potato tubers, with partial least squares regression (PLSR) models developed to predict moisture content from these spectra. The models showed predictive abilities for moisture content with acceptable ratios of prediction to deviation (RPDs) when considering the complete wavelength range (R2 = 0.53, RPD = 1.46, root mean square error (RMSE) = 5.04%) or the β-coefficient wavelength selection technique (R2 = 0.53, RPD = 1.47, RMSE = 5.02%). Furthermore, the prediction ability increased by more than 10% when the model wavelength was narrowed down to 733–970 nm. This study demonstrates the potential of using hyperspectral imaging for the quality assessment of intact, unpeeled potatoes, although further work is required to improve the model quality and implement this approach using remote sensing imagery. [ABSTRACT FROM AUTHOR]

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, "the Bureau"), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau's regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains. [ABSTRACT FROM AUTHOR]

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property. [ABSTRACT FROM AUTHOR]

We constrain viscoelastic Earth rheology and recent ice‐mass change in the northern Marguerite Bay region of the Antarctic Peninsula. Global Positioning System (GPS) time series from Rothera and San Martin stations show bedrock uplift range of ∼−0.8–1.8 mm/year over 1999–2005 and 2016–2020 but ∼3.5–6.0 mm/year over ∼2005–2016. Digital elevation models reveal substantial surface lowering, but at a lower rate since ∼2009. Using these data, we show that an elastic‐only model cannot explain the non‐linear uplift of the GPS sites but that a layered viscoelastic model can. We show close agreement between GPS uplift changes and viscoelastic models with effective elastic lithosphere thickness and upper‐mantle viscosity ∼10–95 km and ∼0.1−9 × 1018 Pa s, respectively. Our viscosity estimate is consistent with a north‐south gradient in viscosity suggested by previous studies focused on specific regions within the Antarctic Peninsula and adds further evidence of the low viscosity upper mantle in the northern Antarctic Peninsula. Plain Language Summary: Over the last few decades, the glaciers of the Antarctic Peninsula have thinned tens to hundreds of meters. This unloading of the solid Earth has produced a viscoelastic response of the solid Earth. By observing this response and comparing it to viscoelastic models combined with ice unloading models, we may infer the viscoelastic properties of the Earth in this region. In this study, we estimate ice mass changes over 15 years using satellite images. Then we analyze estimates of solid Earth displacement from continuous Global Positioning System (GPS) stations attached to bedrock. When compared to the predictions of deformation from viscoelastic models with different Earth properties and ice mass change rates, we find the rate of glacier thinning must have slowed since around 2009–2010 and suggests a thin elastic lithosphere with a lower‐viscosity upper mantle underneath this region. Key Points: GPS‐measured time‐varying uplift rates ranging between ∼1.6 ± 0.8 and ∼6.0 ± 0.6 mm/year over 1999.1–2016.0 in Northern Marguerite BayGlaciers in the region generally show a reduced rate of surface lowering since ∼2009Comparing GPS and modeled viscoelastic deformation suggests a lower‐viscosity upper mantle for this region with possible thin lithosphere [ABSTRACT FROM AUTHOR]

We consider the viscoelastic rheology of the solid Earth under the Antarctic Peninsula due to ice mass loss that commenced after the breakup of the Larsen-B ice shelf. We extend the previous analysis of nearby continuous GPS time-series to include five additional years and the additional consideration of the horizontal components of deformation. They show strong uplift from ∼2002 to 2011 followed by reduced uplift rates to 2018. Modelling the GPS-derived uplift as a viscoelastic response to ongoing regional ice unloading from a new ice model confirms earlier estimates of low upper-mantle viscosities of ∼0.3–3 × 1018 Pa s in this region but allows a wide range of elastic lithosphere thickness. The observed and modelled north coordinate component shows little nonlinear variation due to the location of ice mass change to the east of the GPS sites. However, comparison of the observed and modelled east coordinate component constrains the upper-mantle viscosity to be less than ∼9 × 1018 Pa s, consistent with the viscosity range suggested by the uplift rates alone and providing important, largely independent, confirmation of that result. Our horizontal analysis showed only marginal sensitivity to modelled lithospheric thickness. The results for the horizontal components are sensitive to the adopted plate rotation model, with the estimate based on ITRF2014 suggesting that the sum of residual plate motion and pre-2002 glacial isostatic adjustment is likely less than ∼±0.5 mm yr−1 in the east component. [ABSTRACT FROM AUTHOR]

Our earth is a water planet; nearly two thirds of the earth’s surface is covered by ocean water. But the shortage of fresh water is a major problem in many areas, especially in rural villages near to the sea or islands. Now, renewable energy-based desalination system is rising around the world due to the adverse environmental effect and high-energy requirements of the conventional fuel-based desalination system. This paper describes the prospect of an off-grid stand-alone wave-powered reverse osmosis desalination system for those areas. A simulation model for the prediction of the wave power delivered for a given value of the wave height and period is adopted. Based on the availability of the wave data, the amount of the water produced at different sites of Malaysia can be calculated in this paper. In addition, this paper deals with an economical analysis of wave energy production for reverse osmosis desalination system. [ABSTRACT FROM PUBLISHER]

Energy is one of the most important factors in the socioeconomic development of a country. In a developing country like Malaysia, the development of islands is mostly related to the availability of electric power. As an island surrounded by sea, power generated by wave energy source will become one of the most promising solutions for the electrification of island areas. But, the unpredictable nature of the wave energy source is the main drawback. This paper presents a novel stand-alone wave energy supply system modeling with maximum power tracking and an energy storage-based power management algorithm in MATLAB/Simulink environment. The main contributions of this research work are eliminating the intermittent power generation nature of wave energy and efficiently extract the maximum wave power. In the proposed stand-alone system, a DC–DC bidirectional buck-boost converter controller is used to maintain the constant dc-link voltage. It also accumulates the surplus wave power in the battery bank and supplies this power to the load during the wave power shortage. A three-phase complex vector control scheme voltage source inverter is used to control the load-side voltage in terms of the frequency and voltage amplitude. Based on the simulation results obtained from MATLAB/Simulink, it has been found that the overall system is capable of working under variable weather and load conditions, and it can also able to extract the maximum wave power. [ABSTRACT FROM PUBLISHER]

Energy is one of the most important factors in the socioeconomic development of a country. In a developing country like Malaysia, the development of islands is mostly related to the availability of electric power. Power generated by renewable energy sources has recently become one of the most promising solutions for the electrification of islands and remote rural areas. But high dependency on weather conditions and the unpredictable nature of these renewable energy sources are the main drawbacks. To overcome this weakness, different green energy sources and power electronic converters need to be integrated with each other. This study presents a battery storage hybrid standalone photovoltaic-wind energy power supply system. In the proposed standalone hybrid system, a DC-DC buck-boost bidirectional converter controller is used to accumulates the surplus hybrid power in the battery bank and supplies this power to the load during the hybrid power shortage by maintaining the constant dc-link voltage. A three-phase voltage source inverter complex vector control scheme is used to control the load side voltage in terms of the voltage amplitude and frequency. Based on the simulation results obtained from MATLAB/Simulink, it has been found that the overall hybrid framework is capable of working under variable weather and load conditions. [ABSTRACT FROM AUTHOR]

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples. [ABSTRACT FROM AUTHOR]

Today, the whole world faces a great challenge to overcome the environmental problems related to global energy production. Most of the islands throughout the world depend on fossil fuel importation with respect to energy production. Recent development and research on green energy sources can assure sustainable power supply for the islands. But unpredictable nature and high dependency on weather conditions are the main limitations of renewable energy sources. To overcome this drawback, different renewable sources and converters need to be integrated with each other. This paper proposes a standalone hybrid photovoltaic- (PV-) wave energy conversion system with energy storage. In the proposed hybrid system, control of the bidirectional buck-boost DC-DC converter (BBDC) is used to maintain the constant dc-link voltage. It also accumulates the excess hybrid power in the battery bank and supplies this power to the system load during the shortage of hybrid power. A three-phase complex vector control scheme voltage source inverter (VSI) is used to control the load side voltage in terms of the frequency and voltage amplitude. Based on the simulation results obtained from Matlab/Simulink, it has been found that the overall hybrid framework is capable of working under the variable weather and load conditions. [ABSTRACT FROM AUTHOR]

Smart farming has the potential to overcome the challenge of 2050 to feed 10 billion people. Both artificial intelligence (AI) and the internet of things (IoT) have become critical prerequisites to smart farming due to their high interoperability, sensors, and cutting-edge technologies. Extending the role of responsible leadership, this paper proposes an AI and IoT based smart farming system in Bangladesh. With a comprehensive literature review, this paper counsels the need to go beyond the simple application of traditional farming and irrigation practices and recommends implementing smart farming enabling responsible leadership to uphold sustainable agriculture. It contributes to the current literature of smart farming in several ways. First, this paper helps to understand the prospect and challenges of both AI and IoT and the requirement of smart farming in a nonwestern context. Second, it clarifies the interventions of responsible leadership into Bangladesh's agriculture sector and justifies the demand for sustainable smart farming. Third, this paper is a step forward to explore future empirical studies for the effective and efficient use of AI and IoT to adopt smart farming. Finally, this paper will help policymakers to take responsible initiatives to plan and apply smart farming in a developing economy like Bangladesh. [ABSTRACT FROM AUTHOR]

Agriculture is an important source of greenhouse gas emissions. It is one of the economic sectors that impacts both directly and indirectly towards climate change which contributes to greenhouse gas emissions. There has been a continuous trend of agricultural greenhouse gas emissions reduction technologies, but any step taken in this direction must not negatively affect farm productivity and economics. For the agriculture sector to achieve reduced GHG emission, climate-smart activities and improved food security will be needed for this sector to become a climate-smart landscape. Climate-smart technologies are effective at targeting inputs to the fields, helping to lower greenhouse gas emissions. This article explores the key sources of carbon emissions within the agriculture sector and reviews efficient ways to GHG emission via Smart Farming technology. Based on the public archive GHG datasets, we have found that livestock farming is the largest GHG emission sector among other agricultural sectors and responsible for 70 % of the total emission. Besides, we also show that Queensland is the largest agricultural GHG contributor compared to other states and territories. The article also captures any possible sources within smart farming that may contribute to carbon emissions and suggest ways to reduce GHG emissions. Besides, an Australian-based best management practice approach is discussed to review the emissions reduction strategy based on climate-specific technology to help the farmers and other stakeholders take environmentally-friendly agricultural decisions. [ABSTRACT FROM AUTHOR]

Today, the whole world faces a great challenge to overcome the environmental problems related to global energy production. Most of the islands throughout the world depend on fossil fuel importation with respect to energy production. Recent development and research on green energy sources can assure sustainable power supply for the islands. But unpredictable nature and high dependency on weather conditions are the main limitations of renewable energy sources. To overcome this drawback, different renewable sources and converters need to be integrated with each other. This paper proposes a standalone hybrid photovoltaic- (PV-) wave energy conversion system with energy storage. In the proposed hybrid system, control of the bidirectional buck-boost DC-DC converter (BBDC) is used to maintain the constant dc-link voltage. It also accumulates the excess hybrid power in the battery bank and supplies this power to the system load during the shortage of hybrid power. A three-phase complex vector control scheme voltage source inverter (VSI) is used to control the load side voltage in terms of the frequency and voltage amplitude. Based on the simulation results obtained from Matlab/Simulink, it has been found that the overall hybrid framework is capable of working under the variable weather and load conditions. [ABSTRACT FROM AUTHOR]