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Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MTGS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency. [ABSTRACT FROM AUTHOR]

The performance of solar photovoltaic systems tends to decline if the operating conditions change from the nominal operating cell temperature due to environmental factors. The major factors include temperature and humidity variations, which cause thermal losses and open-circuit voltage drops in photovoltaic panels. This study investigates the correlations between solar photovoltaic performance and environmental factors by quantifying the real-time variables including temperature, humidity, dewpoint temperature, and heat index. Furthermore, the study investigates the difference between real-time measured and theoretically calculated temperature values with the help of weather station data to investigate the impact of temperature difference on the overall percentage power loss of the systems. The panels were installed at three distinct sites (rooftop, parking shed, and ground-mounted) connected to the same grid-tied system. Results reveal that the photovoltaic panel temperature has an inverse relation with the dew temperature and the humidity of the surrounding environment. The heat index value was found slightly less at the peak solar hours during the 24 h cycle of the measurement. The difference and relationship between temperature and humidity are measured and losses are highlighted based on the instantaneous occurrence of variables. Based on a difference between measured and calculated temperature values, results reveal that PV systems faced 27.95%, 5.41%, and 0.82% power losses for ground, roof, and parking installed PV systems, respectively. [ABSTRACT FROM AUTHOR]

Crop yield forecasting is critical in modern agriculture to ensure food security, economic stability, and effective resource management. The main goal of this study was to combine historical multisource satellite and environmental datasets with a deep learning (DL) model for soybean yield forecasting in the United States' Corn Belt. The following Moderate Resolution Imaging Spectroradiometer (MODIS) products were aggregated at the county level. The crop data layer (CDL) in Google Earth Engine (GEE) was used to mask the data so that only soybean pixels were selected. Several machine learning (ML) models were trained by using 5 years of data from 2012 to 2016: random forest (RF), least absolute shrinkable and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and decision tree regression (DTR) as well as DL-based one-dimensional convolutional neural network (1D-CNN). The best model was determined by comparing their performances at forecasting the soybean yield in 2017-2021 at the county scale. The RF model outperformed all other ML models with the lowest RMSE of 0.342 t/ha, followed by XGBoost (0.373 t/ha), DTR (0.437 t/ha), and LASSO (0.452 t/ha) regression. However, the 1D-CNN model showed the highest forecasting accuracy for the 2018 growing season with RMSE of 0.280 t/ha. The developed 1D-CNN model has great potential for crop yield forecasting because it effectively captures temporal dependencies and extracts meaningful input features from sequential data. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between spatial distribution of economic activity and local markets of primary sector in panel of Siberian regions over the period 2005-2018, using measure of localization that is Theil index considering two hierarchical levels. Section 2 describes the data and method using geospatial toolset that we have proposed, discusses the construction model for study spatial concentration spillovers between local markets. In Section 3 we study how the processes of urbanization and growth of population density in cities and agricultural development in rural areas affect the formation of local housing markets, and as a consequence, the associated local markets for goods and resources; and the last section concludes. Keywords: economic growth, regional economics, agricultural industry, local markets, spillovers, housing market

Image captured in bad weather conditions confines scene prominence, appears grey and diminishes image contrast. This usually happens due to atmospheric dispersing phenomenon that affects the quality of outdoor computer vision frameworks. This deprivation relies on the gap between the object point and the camera and mostly differs for every pixel present in an image. Transmission coefficients, which state the aforementioned dependence are used to manage the haze level in each pixel. Our algorithm is subject to the presumption that the haze-free image forms clusters given in RGB space. The pixels over the image plane are often found at different locations and their distance from camera also differs. These fluctuating distances give rise to different transmission coefficients. Consequently, these colour clusters form the certain lines of colours in RGB space known as haze lines. The first step is to assure accurate estimation of the atmospheric light, for this an additional wavelet channel is recommended, based on frequency subdivision. The next step is to separate the average gradients present in the foggy regions of an image according to the frequency criteria. Lastly, the haze-free image information is retrieved by utilising the atmospheric scattering model on low and high frequencies according to the edges of unpredicted change in the field depth. Using the non-local and frequency information retrieval, proposed algorithm recovers the haze-free image, efficiently. [ABSTRACT FROM AUTHOR]

The Multi-View extension of High Efficiency Video Coding (MV-HEVC) has improved the coding efficiency of multi-view videos, but this comes at the cost of the extra coding complexity of the MV-HEVC encoder. This coding complexity can be reduced by efficiently reducing time-consuming encoding operations. In this work, we propose two methods to reduce the encoder complexity. The first one is Early Coding unit Splitting (ECS), and the second is the Efficient Reference Picture Selection (ERPS) method. In the ECS method, the decision of Coding Unit (CU) splitting for dependent views is made on the CU splitting information obtained from the base view, while the ERPS method for dependent views is based on selecting reference pictures on the basis of the temporal location of the picture being encoded. Simulation results reveal that our proposed methods approximately reduce the encoding time by 58% when compared with HTM (16.2), the reference encoder for MV-HEVC. [ABSTRACT FROM AUTHOR]

Allama Iqbal Open University (AIOU) is Pakistan’s largest distance learning institute, providing education to 1.4 million students. This is a fairly large setup across a country where students are highly geographically distributed. Currently, the system works using a manual approach, which is not efficient. Allocation of tutors and study centers to students plays a key role in creating a better learning environment for distance learning. Assigning tutors and study centers to distance learning students is a challenging task when there is a huge geographic spread. Using geospatial technologies in open and distance learning can fix allocation problems. This research analyzes real data from the twin cities Islamabad and Rawalpindi. The results show that geospatial technologies can be used for efficient and proper resource utilization and allocation, which in turn can save time and money. The overall idea fits into an improved distance learning framework and related analytics. [ABSTRACT FROM AUTHOR]