Yield Estimation Research Articles

Joint Inversion of Regional Waveform, First-Motion Polarity, and Synthetic Aperture Radar Surface Displacement for the Fourth and Sixth North Korean Declared Nuclear Explosions

ABSTRACT This study analyzed the Democratic People’s Republic of Korea’s (DPRK) fourth (DPRK4, 6 January 2016 Mw 4.49) and sixth (DPRK6, 7 September 2017 Mw 5.2) declared nuclear tests, employing a joint seismic and Interferometric Synthetic Aperture Radar (InSAR) inversion to improve understanding of these events and enhance moment tensor (MT) inversion capabilities. The recent efforts have focused on employing seismic waveform and InSAR geodetic deformation data separately to analyze these and the previous nuclear tests (e.g., Chiang et al., 2018; Myers et al., 2018; Wang et al., 2018). Building upon our previous work (Chi-Durán et al., 2021), we performed a joint regional waveform, first-motion (FM) polarity, and surface displacement inversion, which demonstrated improved source-type discrimination, a revised MT solution with reduced scalar moment uncertainty, and an independently constrained location. In this article, we build on the previous results for DPRK6 by including an analysis using a four-layered velocity model with free-surface topography to compute the near-source static deformation Green’s functions. The model consists of a 50 m basalt layer (VP=2.07 km/s, VS=1.2 km/s), a 250 m stratified volcanic deposit layer (VP=1.73 km/s, VS=1.0 km/s), a 700 m weathered granodiorite layer (VP=2.5 km/s, VS=1.3 km/s), and a granodiorite half-space (VP=5.35 km/s, VS=3.09 km/s). The half-space shares the velocity of the regional MDJ2 velocity model (Ford et al., 2010), which has proven effective for waveform inversion in the region. This model considers the range of reported values for various lithologies and weathering effects. Our findings show that using the layered velocity model enhances the recovery of source location and depth for both the explosions by improving fits and reducing uncertainties. The joint inversion also improves source-type discrimination and better constrains the scalar seismic moment necessary for downstream yield estimation.

Mapping annual 10-m soybean cropland with spatiotemporal sample migration

China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This method uses a feature space of six integrals from the crops’ phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Satellite-based soybean yield prediction in Argentina: A comparison between panel regression and deep learning methods

The accurate prediction of soybean yield is vital for global food market stabilization and food security. Recent advancements in remote sensing technology have significantly amplified interest in leveraging satellite-based methods for predicting crop yield. These methods offer in-season yield estimates. By utilizing this timely information, decision-makers can formulate strategic, well-informed choices that preemptively mitigate potential food price hikes, ultimately bolstering food security. While simple regression models have been widely utilized for satellite-based yield prediction, researchers have recently begun to explore the use of deep learning algorithms. This study compares the performance of panel regression and deep learning models for in-season soybean yield prediction at the Department (county-equivalent) level in Argentina. Data sources include the latest soybean land use products and MODIS bi-weekly vegetation index products. Results indicate that deep learning models significantly outperform panel regression. Deep learning Long Short-Term Memory (LSTM) models, which incorporate attention mechanism and a series of peak NDVI images, generate more accurate and time-sensitive predictions. Among competing LSTM models, the one with attention mechanism applied to the entire growing season's NDVI data yields the highest prediction accuracy, with a Root Mean Square Error (RMSE) of 505.78 kg/ha and Normalized Root Mean Square Error (NRMSE) of 0.0726. The LSTM model with attention on the three highest NDVI images attains a satisfactory prediction accuracy (RMSE = 627.28 kg/ha, NRMSE = 0.089) six weeks prior to harvest. This study presents a robust model for predicting crop yields, promoting sustainable production of soybeans and facilitating knowledgeable choices among farmers and policymakers.

Enhancing environmental DNA metabarcoding from marine ecosystems: Impact of filter type, storage method, and storage time on the assessment of fish alpha and beta diversity

AbstractThe collection of environmental DNA (eDNA) and subsequent metabarcoding are useful tools for assessing marine fish biodiversity noninvasively. It is of particular importance to evaluate biodiversity in regions that are hard to access and thus less well studied. Sampling and preservation methods tailored to the specific circumstances are required. Aquatic eDNA is often captured on filters made of different materials and pore sizes, and subsequently stored under divergent conditions for varying periods of time. Previous studies on multispecies detection in marine systems have primarily focused on capture and extraction effects. Our study, in contrast, examined the effects of filter type, storage method, and storage time on DNA yield, alpha (i.e., ZOTU richness) and beta diversity (i.e., ZOTU composition) recovered from a marine ecosystem in Shark Bay, Western Australia. We compared two different filter types (cellulose‐nitrate filters with pore sizes of 0.45 μm; glass‐fiber filters with pore sizes of 0.1 μm), two storage methods (preservation in Longmire's solution and drying, respectively), various storage times (30–68 days) on two metabarcoding assays using different fish‐specific primers. Our results showed that storage time decreased DNA yield and affected alpha and beta diversity estimates. Cellulose‐nitrate filters stored in Longmire's solution proved to be the best combination with the smallest decrease in DNA yield, no effect on alpha diversity and consistent community compositions. Storing glass‐fiber filters in Longmire's solution led to a decrease in eDNA yield and alpha diversity estimates with increasing storage time. Furthermore, the largest change in beta diversity for each metabarcode was found for glass‐fiber filters regardless of storage method. Our results highlight the importance of considering storage time and interactions between storage method and filter when analyzing eDNA results, especially when storing samples for an extended time period or comparison of samples stored for different durations.

Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)

This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management.

A density map-based method for counting wheat ears.

Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field. There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios. Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information. The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

DL-RSM: Deep learning-integrated Response Surface Methodology for positive and negative-ideal environmental conditions estimation for crop yield

Understanding the most suitable and unsuitable environmental scenarios for crop production is critical to ensuring food security and sustainability. In this direction, the presented work proposes a novel approach, DL-RSM, to identify the optimized levels of environmental factors for wheat yield by integrating the Deep Learning technique with Response Surface Methodology (RSM). The uniqueness of this approach lies in its utilization of a 1-Dimensional Convolutional Neural Network model for response surface modelling. Additionally, the study has investigated the combination of numerical methods-based partial derivative calculation with a deep learning model. Specifically, our approach incorporates a symmetric difference quotient-based method for calculating partial derivatives, ultimately leading to the determination of optimal parameter values. Besides, multiple positive and negative ideal solutions are obtained from the modelled response surface using a modified gradient-based method. The effectiveness of our approach is illustrated through a case study on finding the optimized parameter values for the district-wise wheat yields of the Indo-Gangetic plains of India. The study results demonstrate the combinations of monthly rainfall levels and min. and max. temperature variations correspond to maximizing and minimizing wheat yield in the target region. The presented results provide significant insights into the relationship between wheat yields and the considered environmental factors that have not been sufficiently covered in the existing literature. The proposed methodology can benefit agricultural scientists, policymakers and farmers by offering an innovative approach to maximize crop yield and minimize resource utilization. Moreover, the study findings provide novel insights into the development of similar optimization strategies for other crops and systems.

YOLOv5s-CEDB: A robust and efficiency Camellia oleifera fruit detection algorithm in complex natural scenes

To solve the problem of poor recognition accuracy caused by various colors, uneven distribution, and occlusion by branches and leaves of Camellia oleifera fruits under natural growth conditions, this study proposes an improved deep learning network, YOLOv5s-CEDB, for Camellia oleifera fruit detection based on YOLOv5s. Coordinate Attention Mechanism (CoordAtt), Deformable Convolution (DConv), and Explicit Visual Center (EVC) are introduced to enhance the network’s local and global feature extraction performance. To improve the detection performance for small and dense targets, the feature fusion module of the network was replaced with the designed light-bidirectional feature pyramid (light-BiFPN) structure. GhostConv was used to reduce the parameters and inference speed of the structure. A dataset with different light conditions, colors, and density levels of Camellia oleifera fruits was established, and performance evaluation experiments were conducted. Experimental results showed that the mean Average Precision (mAP) and F1-score of the designed YOLOv5s-CEDB network reached 91.4 % and 89.6 %, respectively, which were 2.6 % and 3.6 % higher than those of the original YOLOv5s model, respectively, and the influencing frame rate arrived at 37.6 FPS. Under different colors, distribution densities, occlusion scenarios, and light intensities, the detection accuracy of the YOLOv5s-CEDB network model was significantly better than those of the YOLOv5s, YOLOv8s and Faster-RCNN networks. It was verified that the proposed YOLOv5s-CEDB network could significantly improve the accuracy and stability of Camellia oleifera fruit detection, satisfying the requirements of yield estimation and intelligent harvesting.

Crop Yield Estimation using Improved Salp Swarm Algorithm based Feature Selection

Crop yield estimation is the art of yield prediction before harvest and it is essential for planning and making conclusive agricultural policies. The forecasting of crop yield is essential in optimal nutrient management, crop insurance, crop market planning and harvest management. However, the crop yield estimation is considered as a challenging task because of huge amount of abundant information exists in the crop data. Therefore, an effective feature selection is required to be developed for removing the redundant attributes. In this research, an Improved Salp Swarm Algorithm (ISSA) based feature selection for an effective crop yield estimation. The Opposition Based Learning (OBL) and Local Search Algorithm (LSA) are incorporated in the ISSA’s initialization and exploitation phase for selecting optimum feature subset. The selected features from the ISSA are used to enhance the classification using Modified Long Short Term Memory (MLSTM) classifier. The performance of the ISSA-MLSTM is analyzed using accuracy, precision, recall, F-score, Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The existing researches such as Ensemble approach and MLSTM are used to evaluate the ISSA-MLSTM. The accuracy of the ISSA-MLSTM is 99% that is high when compared to the MLSTM.

Harvesting Efficiency: The Rise of Drone Technology in Modern Agriculture

The articles delves into the transformative impact of drone technology on modern agriculture, specifically focusing on its role in enhancing harvesting efficiency. Drones, equipped with advanced sensors and imaging capabilities, have revolutionized traditional farming practices by offering real-time data collection and analysis. This abstract explores how drones facilitate precision agriculture through the optimization of harvesting processes, including crop monitoring, yield estimation, and targeted harvesting. By leveraging drones, farmers can make informed decisions to improve productivity, minimize waste, and maximize yield. The abstract highlights the integration of drone technology into agricultural operations as a sustainable solution to meet the growing demand for food production while minimizing environmental impact. Moreover, it underscores the need for further research and development to fully harness the potential of drones in modern agriculture and address challenges such as regulatory hurdles and cost-effectiveness.

Optimal Timing of Carrot Crop Monitoring and Yield Assessment Using Sentinel-2 Images: A Machine-Learning Approach

Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha−1 (R2 ≥ 0.82 with Scenario 5) to 26.2 t ha−1 (R2 ≤ 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha−1 (i.e., 12.4%) and 11.4 t ha−1 (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production.

Mango Tree Dataset for Yield Estimation: Some Exploratory Analysis

Objectives: Employing computer vision methods for yield estimation requires standard datasets. However, unavailability of such a dataset in the literature limits the researchers from evaluating and comparing the performance of their proposed algorithms. In this paper, a benchmark of mango tree dataset is introduced. Methods: The dataset is gathered over a 5-month period, starting from the fruit's blossoming stage and ending with its ripening stage. There are 21,000 photos of 4 distinct mango tree varieties in the dataset. From each cultivar, images are captured with different views, distances, and daylight conditions. Further, preprocessing, and exploratory analysis of the dataset are carried out by extracting a few global features such as colors, textures, and histograms for intra-class and inter-class mango trees. Findings: The analysis of the collected dataset with different color layers by extracting a few global features and classification of the cultivars of mango trees and, based on the results obtained, the optimal layer of color is attained for the further yield estimation process. Novelty: Exploratory analysis of a novel temporal mango crop dataset is executed, and a color analysis classification method is proposed to aid in the early estimation of mango fruit crop yields. Keywords: Mango Fruit, Yield Estimation, Temporal Dataset, Feature Extraction, Tree Classification

Estimation of standard heterosis and heterotic grouping of elite sorghum [Sorghum bicolor (L) Moench] inbred lines under moisture stress conditions

The development and selection of appropriate parents are prerequisites for sorghum hybrid variety development. The lack of a broad genetic base is the most significant constraint to sorghum crop improvement. The assignment of sorghum germplasm lines to appropriate heterotic groups is critical for increasing sorghum productivity. The experiment was conducted to determine the amount of standard heterosis and to categorize sorghum inbred lines into distinct heterotic groups. In the 2019 main cropping season, 42 sorghum genotypes were tested using an alpha-lattice design with two replications. Combined analysis of variance showed that there was a highly significant difference (p < 0.01) among the genotypes for all studied traits. The maximum grain yield was obtained from the hybrids 4x14 (6.32 tha-1), followed by the hybrids 8x15 (5.92 tha-1), 1x15 (5.88 tha-1), 13x14 (5.78 tha-1) and 6x15 (5.57 tha-1), with an average value of 5.0 tha-1. Similarly, the 4x14 hybrid exhibited a maximum grain yield with 30.71 % heterosis, which was greater than the standard check (ESH-4) for grain yield. The two heterotic (A and B) groups were identified based on their specific combining ability effects, whereas three heterotic groups were identified based on their general combining ability effects to develop superior hybrids from broad base and suitable parents. Finally, based on yield performance, heterotic response and combining ability estimates for grain yield and its components, the hybrid crosses 4x14, 8x15, 1x15, 11x14, 11x15, 13x14, and 6x15 were found to be the most promising and potential hybrids that could be exploited commercially after critical evaluation for their superiority and yield stability across locations over the years.

Rice Yield Estimation Using Multi-Temporal Remote Sensing Data and Machine Learning: A Case Study of Jiangsu, China

Effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. However, traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional rice yields. In this study, we employed four machine-learning techniques: partial least squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and back propagation neural network (BPNN). We combined these methods with multi-temporal rice NDVI (normalized difference vegetation index) data for rice yield estimation. Following an accuracy evaluation and a spatial analysis, the key findings of our study are as follows. (1) The RFR model emerged as the most accurate for rice yield estimation, achieving an R2 of 0.65, an RMSE of 388.79 kg/ha, and an rRMSE of 4.48%. While PLSR and SVR demonstrated comparable accuracy, they were both inferior to RFR. (2) Using the top seven predictors with the highest importance rankings as inputs for the RFR model (NDVI values on the 6th, 17th, 33rd, 44th, 71st, 90th, and 106th days after the rice transplanting stage) achieved comparable accuracy while reducing information redundancy. (3) The proposed model demonstrated good spatial applicability (MI = −0.03) for rice yield estimation in Jiangsu, China. (4) A high spatial resolution yearly rice yield dataset (1 km) spanning from 2001 to 2020 was generated using the proposed model and is accessible on the Zenodo database. In conclusion, this study has demonstrated the efficacy of combining multi-temporal remote sensing data with machine-learning techniques for accurate rice yield estimation, thereby aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season.

Evaluation of the Effect of Sentinel-1 SAR and Environmental Factors in Alfalfa Yield and Quality Estimation

Alfalfa is one of the most widely cultivated perennial legume crops used as feedstock for animals. Efficiently estimating alfalfa yield and quality traits before harvesting is critical for the decision-making process regarding precision management activities and harvesting time to ensure high profitability. Satellite-based radar is a powerful tool in remote sensing for crop monitoring because it provides high-quality data regardless of weather conditions. Therefore, this study aims to investigate the potential use of satellite radar features and environmental factors in estimating alfalfa yield and quality. Alfalfa yield and quality traits, including dry matter yield (DMY), crude protein (CP), neutral detergent fiber (NDF), NDF digestibility (NDFD), and acid detergent fiber (ADF), were collected over 16 alfalfa fields from 2016 to 2021, leading to 126 samples in total. Sentinel-1 radar backscattering coefficients and environmental factors were collected for all the fields across all growing seasons. Five commonly used machine learning models were established to estimate each alfalfa trait separately. The results show that the Extreme Gradient Boosting model consistently performed the best for all alfalfa traits. The accuracy of the DMY estimates is acceptable, with an average R2 of 0.67 and an RMSE of 0.68 tons/ha. The best result for estimating CP was an average R2 of 0.70 and an RMSE of 1.63% DM. In estimating alfalfa fiber indicators (i.e., ADF, NDF, and NDFD), we achieved the highest average R2 values of 0.54, 0.62, and 0.56, respectively. Overall, this study demonstrated the potential use of environmental factors for alfalfa yield and quality estimation in-field before harvesting. However, the Sentinel-1 radar backscattering coefficients did not make significant contributions to improving the estimation performance, compared to the environmental factors.

Groundwater recharge is diffuse in semi-arid African drylands: Evidence from highly instrumented observatories

We use two comprehensively instrumented field observatories to understand groundwater recharge processes in African drylands. The observatories are located on crystalline basement geology in semi-arid parts of Ghana and Burkina Faso, aridity indices 0.43 and 0.29, respectively, and we report 2017–2019 observations. Groundwater recharge was quantified by inverse water table fluctuation models using specific yield estimates derived from magnetic resonance soundings. Evidence for recharge drivers and mechanisms comes from high resolution meteorological observations, soil moisture (logged hourly and weekly along hillslope transects), overland flow plots, river stage, and stable isotopes of O and H in rainfall events and groundwater. Groundwater recharge varied between 87 and 175 mm/y, i.e. 7–15 % of annual rainfall. Rainfall was twice the volume of water lost via actual evapotranspiration across the four peak months (Jun-Sep) of the monsoon. This seasonal water surplus of ∼ 350 mm/y is not characterised by the annual scale of the aridity index. Overland flow was rare and soil moisture deficits were overcome at all monitoring locations. Large rainfall events only produced appreciable recharge when the antecedent soil moisture was close to field capacity, yet always produced large responses in river stage. Stable isotopes of O and H in groundwater indicate no evidence of evapotranspiration prior to infiltration and their composition is akin to depleted isotopic rainfall at the monsoon peak. Stable isotopes indicate recharge season timing and not a relationship between intense rainfall and groundwater recharge. We contend that the mechanism for groundwater recharge is predominantly diffuse in these semi-arid African settings.

RICE-YOLO: In-Field Rice Spike Detection Based on Improved YOLOv5 and Drone Images

The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.

Analysis and Selection of Optimal Perovskite/Silicon Tandem Configuration for Building Integrated Photovoltaics Based on Their Annual Outdoor Energy Yield Predicted by Machine Learning

Accurate estimation of annual output energy yield () for perovskite/silicon (PSK/cSi) tandem solar cells is pivotal in assessing their suitability for building‐integrated photovoltaics (BIPV). This study pioneers five machine learning models of ensembles of trees, Gaussian process regressions, regression trees, support vector machines, and artificial neural networks (ANN) to predict output power density and compute for 2T, 3T, and 4T PSK/cSi tandem configurations in Japan's outdoor conditions. Seven predictive inputs of visible‐light solar irradiance, near‐infrared‐light solar irradiance, incident solar spectrum angle, solar module temperature, perovskite thickness, perovskite bandgap, and terminal of tandem configuration (T) drive the machine learning models. These models optimize predictions using k‐fold cross‐validation and Bayesian algorithms, showcasing superior precision in prediction compared to prior models. The ANN model emerges as the best model, displaying the minimal error in predicting , used to estimate across five Japanese locations (Gifu, Naganuma, Okinoerabu, Tosu, and Tsukuba). Results from these locations in blue‐rich solar spectrum zones identify the 4T PSK/cSi tandem configuration, featuring the most outstanding mean maximal (93.63, 263.02, 153.59, and 91.75 kWh m−2 for the east, rooftop, south, and west directions), as the prime candidate for BIPV applications.

A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images

Construction waste is unavoidable in the process of urban development, causing serious environmental pollution. Accurate assessment of municipal construction waste generation requires building construction waste identification models using deep learning technology. However, this process requires high-quality public datasets for model training and validation. This study utilizes Google Earth and GF-2 images as the data source to construct a specific dataset of construction waste landfills in the Changping and Daxing districts of Beijing, China. This dataset contains 3,653 samples of the original image areas and provides mask-labeled images in the semantic segmentation domains. Each pixel within a construction waste landfill is classified into 4 categories of the image areas, including background area, vacant landfillable area, engineering facility area, and waste dumping area. The dataset contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities. The pixel-level semantic segmentation labels are provided to quantify the construction waste yield, which can serve as the basic data for construction waste extraction and yield estimation both for academic and industrial research.

Estimates of lithium mass yields from produced water sourced from the Devonian-aged Marcellus Shale

Decarbonatization initiatives have rapidly increased the demand for lithium. This study uses public waste compliance reports and Monte Carlo approaches to estimate total lithium mass yields from produced water (PW) sourced from the Marcellus Shale in Pennsylvania (PA). Statewide, Marcellus Shale PW has substantial extractable lithium, however, concentrations, production volumes and extraction efficiencies vary between the northeast and southwest operating zones. Annual estimates suggest statewide lithium mass yields of approximately 1160 (95% CI 1140–1180) metric tons (mt) per year. Production decline curve analysis on PW volumes reveal cumulative volumetric disparities between the northeast (median = 2.89 X 107 L/10-year) and southwest (median = 5.56 × 107 L/10-year) regions of the state, influencing lithium yield estimates of individual wells in southwest [2.90 (95% CI 2.80–2.99) mt/10-year] and northeast [1.96 (CI 1.86–2.07) mt/10-year] PA. Moreover, Mg/Li mass ratios vary regionally, where NE PA are low Mg/Li fluids, having a median Mg/Li mass ratio of 5.39 (IQR, 2.66–7.26) and SW PA PW is higher with a median Mg/Li mass ratio of 17.8 (IQR, 14.3–20.7). These estimates indicate substantial lithium yields from Marcellus PW, though regional variability in chemistry and production may impact recovery efficiencies.