Interpolation Of Data Research Articles

Traffic flow prediction based on machine learning

The rapid development of intelligent transportation systems requires accurate prediction of short-term traffic flow. However, the nonlinearity, uncertainty, and spatiotemporal variability of traffic flow make it difficult for traditional traffic flow prediction methods to achieve ideal results. This study uses machine learning methods to improve the accuracy of prediction and expand features by evaluating the clustering effect of the k-means algorithm through data dimensionality reduction, interpolation of missing data, and the silhouette coefficient method. The 5-fold cross-validation and grid search methods were used to optimize the hyperparameters, and the Lasso regression model, ridge regression model and random forest regression model were compared. It was found that the feature expansion of the random forest had the highest fitting coefficient and the smallest error. This improves prediction accuracy, provides a valuable reference for intelligent traffic flow prediction, and has the potential for further optimization in feature selection, model design, and traffic control strategies.

Hermite interpolation with retractions on manifolds

Interpolation of data on non-Euclidean spaces is an active research area fostered by its numerous applications. This work considers the Hermite interpolation problem: finding a sufficiently smooth manifold curve that interpolates a collection of data points on a Riemannian manifold while matching a prescribed derivative at each point. A novel procedure relying on the general concept of retractions is proposed to solve this problem on a large class of manifolds, including those for which computing the Riemannian exponential or logarithmic maps is not straightforward, such as the manifold of fixed-rank matrices. The well-posedness of the method is analyzed by introducing and showing the existence of retraction-convex sets, a generalization of geodesically convex sets. A classical result on the asymptotic interpolation error of Hermite interpolation is extended to the manifold setting. Finally numerical experiments on the manifold of fixed-rank matrices and the Stiefel manifold of matrices with orthonormal columns illustrate these results and the effectiveness of the method.

Use of HANTS algorithm in Sentinel-2 images for interpolation of missing data: a case-study for soy agricultural areas in periods of growth and harvesting

Abstract. Information about the current state of an agricultural culture can be fundamental in decision making, varying from quantity of fertilizers to be used in an area to estimating the productivity of a harvest in a period to establish credits payment. Satellite images with reasonable spatial resolution like Sentinel-2 are a powerful tool to visualize agricultural areas production, especially with the application of multispectral indexes like the Normalized Difference Vegetation Index (NDVI). Yet, due to the limitation of temporal resolution of satellites and periods with large cloudy weather can make the analysis difficult. This study proposed the use of the harmonic analysis time series algorithm (HANTS) to aid the incompleteness of time series obtained by the available satellite images in a case study of soy culture in Brazil. By applying the algorithm, a harmonic interpolation is obtained to produce a full daily NDVI time series from the beginning to end of the study period, facilitating further analysis to obtain metrics of interest. In this study, time series with gaps on specified periods are created based on full time series, then interpolated and compared with the real ones based on root mean squared error (RMSE) to access its accuracy.

Spatio-temporal reconstruction of droplet impingement dynamics by means of color-coded glare points and deep learning

Abstract The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas–liquid interface on the basis of monocular images obtained via optical measurement techniques. The method is tested and evaluated at the example of liquid droplets impacting on structured solid substrates. The droplet dynamics are captured through high-speed imaging in an extended shadowgraphy setup with additional glare points from lateral light sources that encode further three-dimensional information of the gas–liquid interface in the images. A neural network is trained for the physically correct reconstruction of the droplet dynamics on a labeled dataset generated by synthetic image rendering on the basis of gas–liquid interface shapes obtained from direct numerical simulation. The employment of synthetic image rendering allows for the efficient generation of training data and circumvents the introduction of errors resulting from the inherent discrepancy of the droplet shapes between experiment and simulation. The accurate reconstruction of the three-dimensional shape of the gas–liquid interface during droplet impingement on the basis of images obtained in the experiment demonstrates the practicality of the presented approach based on neural networks and synthetic training data generation. The introduction of glare points from lateral light sources in the experiments is shown to improve the reconstruction accuracy, which indicates that the neural network learns to leverage the additional three-dimensional information encoded in the images for a more accurate depth estimation. By the successful reconstruction of obscured areas in the input images, it is demonstrated that the neural network has the capability to learn a physically correct interpolation of missing data from the numerical simulation. Furthermore, the physically reasonable reconstruction of unknown gas–liquid interface shapes for drop impact regimes that were not contained in the training dataset indicates that the neural network learned a versatile model of the involved two-phase flow phenomena during droplet impingement.

Deep Learning-Based In Situ Micrograph Synthesis and Augmentation for Crystallization Process Image Analysis

Deep learning-based in situ imaging and analysis for crystallization process are essential for optimizing product qualities, reducing experimental costs through real-time monitoring, and controlling the process. However, large and high-quality annotated datasets are required to train accurate models, which are time consuming. Therefore, we proposed a novel methodology that applied image synthesis neural networks to generate virtual information-rich images, enabling efficient and rapid dataset expansion while simultaneously reducing annotation costs. Experiments were conducted on the L-alanine crystallization process to obtain process images and to validate the proposed workflow. The proposed method, aided by interpolation augmentation and data warping augmentation to enhance data richness, utilized only 25% of the training annotations, consistently segmenting crystallization process images comparable to those models utilizing 100% of the training data annotations, achieving an average precision of nearly 98%. Additionally, based on the analysis of Kullback–Leibler divergence, the proposed method demonstrated excellent performance in extracting in situ information regarding aspect ratios and crystal size distributions during the crystallization process. Moreover, its ability to leverage expert labels with a four-fold enhanced efficiency holds great potential for advancing various applications in crystallization processes.

DiffMAR: A Generalized Diffusion Model for Metal Artifact Reduction in CT Images.

X-ray imaging frequently introduces varying degrees of metal artifacts to computed tomography (CT) images when metal implants are present. For the metal artifact reduction (MAR) task, existing end-to-end methods often exhibit limited generalization capabilities. While methods based on multiple iterations often suffer from accumulative error, resulting in lower-quality restoration outcomes. In this work, we innovatively present a generalized diffusion model for Metal Artifact Reduction (DiffMAR). The proposed method utilizes a linear degradation process to simulate the physical phenomenon of metal artifact formation in CT images and directly learn an iterative restoration process from paired CT images in the reverse process. During the reverse process of DiffMAR, a Time-Latent Adjustment (TLA) module is designed to adjust time embedding at the latent level, thereby minimizing the accumulative error during iterative restoration. We also designed a structure information extraction (SIE) module to utilize linear interpolation data in the image domain, guiding the generation of anatomical structures during the iterative restoring. This leads to more accurate and robust shadow-free image generation. Comprehensive analysis, including both synthesized data and clinical evidence, confirms that our proposed method surpasses the current state-of-the-art (SOTA) MAR methods in terms of both image generation quality and generalization.

Feline sporotrichosis: occurrence in the Metropolitan Region of the municipality of Recife and anatomical distribution of lesions on cats’ body

Sporotrichosis is a fungal infection of worldwide importance caused by a saprophyte fungus of the genus Sporothrix. The objective of this study was to report the occurrence of feline sporotrichosis in the Metropolitan Region of the city of Recife and the anatomical distribution of lesions on the bodies of cats. Stray cats (n=100) with suggestive sporotrichosis lesions were examined. Material from the lesion was collected from all patients using an imprint and/or sterile swab for cytology. The material was placed on a glass slide and stained using rapid panoptic staining. Data on the localization of each skin lesion were recorded, and cigar-shaped yeast structures were searched at the microscopical examination. A heat map graphically represented the anatomical distribution of each lesion. In 70% (70/100) of samples were visualized fungal structure compatible with Sporothrix spp. There was a predominance of positive males (?2=6.691; p=0.0097). Skin lesions related to Sporothrix spp. infections were unevenly distributed throughout the animal's body. Based on the interpolation of data, the chance for detecting lesions was higher on the nose, anterior paw, and ears than on other body sites (H=40.0043; p=0.0000). Because of the high positivity in these cats, measures of health vigilance are imperative to reduce the risk of animal and human infection.

Simulation-based adaptive optimization for passenger flow control measures at metro stations

Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.

Efficient equivalent-layer technique for processing irregularly spaced magnetic data on uneven surfaces

We present a new rapid and robust equivalent layer method for processing large magnetic data sets that can deal with irregularly spaced data points on undulating surfaces. Our method estimates an equivalent layer by numerically solving the upward continuation integral for a given set of magnetic data. While this estimated layer allows performing specific spatial transformations such as interpolation and continuations of any potential-field data, it cannot be used to perform conventional phase transformations on magnetic data, such as the reduction to the pole, because it requires a magnetic moment distribution over the equivalent layer of dipoles. To overcome this drawback, we transform the estimated equivalent layer into a planar equivalent layer of dipoles that equally reproduces the observed magnetic data. To carry out this transformation, we first use the fact that the estimated layer obtained by solving the upward continuation integral is a scaled version of the magnetic data on the layer plane, i.e., it can be considered a harmonic function. Then we use the upward continuation integral to continue the estimated layer into a planar and regular grid, which results in a second equivalent layer. Finally, we apply a fast 2D discrete convolution to transform this second layer into a planar equivalent layer of dipoles that also fits the observed magnetic data. Applications to synthetic and real data show that our methodology is able to efficiently process magnetic data, combining the robustness of accommodating irregularly spaced data with the ability to perform phase transformations.

Development and Validation of a Predictive Model for Growth of Salmonella Infantis in Ground Turkey

Most retail samples (25 g) of ground turkey contain no or low levels of Salmonella. However, temperature abuse after retail can lead to spread and growth of Salmonella in the package. In addition, it can lead to levels that pose a significant risk of salmonellosis. This is especially true when the serotype is a top human clinical isolate, like Infantis. Therefore, the current study was undertaken to develop and validate a predictive model for the growth of Salmonella Infantis in ground turkey subjected to temperature abuse. The purpose was to fill a gap for serotype-specific data and models in risk assessments for this pathogen and food combination. Storage trials with a low initial inoculum (0.85 log10) of Salmonella Infantis in commercial ground turkey samples (0.2 g) with native microflora were conducted at 16–40 °C for 0–28 h. Salmonella was enumerated in ground turkey samples using an automated, whole sample enrichment, miniature, most probable number (MPN) assay. The MPN data were fitted to a three-phase linear primary model. Secondary models for primary model parameters were developed and used in the primary model to create a tertiary model that predicted the growth of Salmonella Infantis in ground turkey as a function of time and temperature. Data and tertiary model predictions were evaluated using the test data, model performance, and model validation criteria of the Acceptable Prediction Zones method in the Validation Software Tool. The tertiary model predictions were considered to have acceptable bias and accuracy when the proportion of residuals (observed − predicted) in the partly and fully acceptable prediction zones (pAPZ) was ≥0.7. The overall pAPZ of the tertiary model was 0.866 for dependent data (n = 406) and 0.853 for independent data for interpolation (n = 177). However, there were local prediction problems that limited the validated prediction range to a region from 0 to 8 h at 16–40 °C. Nonetheless, this validation range was sufficient to simulate temperature abuse of ground turkey during meal preparation in the consumers’ home. Thus, the model fills an important data and modeling gap in risk assessments for Salmonella and ground turkey. Additional data are needed to repair and fully validate the model.

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

Surface Deformation Analysis by Fusion of Multi-temporal and Multi-source Data under Disaster Emergency Conditions

Abstract. Rapid analysis of surface deformation is crucial for rescue operations following natural disasters. However, the lack of recent terrain data and the discrepancy between data types and field-collected data often hinder timely surface deformation analysis. To enhance data usability, this paper proposes an analytical method that integrates multiple sources of remote sensing data, including satellite data, oblique photography data, and LiDAR data. By merging oblique images with grayscale point clouds, true-color point clouds are generated. The method optimizes old data through threshold segmentation and median filtering, then converts and unifies the resolution of multi-source data via data interpolation. Elevation interpolation matrices are employed for overlay analysis, and a combination of bilateral filtering and threshold processing is used. This groundbreaking approach enables the completion of surface deformation analysis in emergency geospatial surveys and has been validated in various typical regions, demonstrating its application potential across different surface environments. Experimental results indicate that this method can quickly utilize multi-temporal and multi-source remote sensing data to effectively identify surface deformations following natural disasters.

Augmented Reality for Air Quality Monitoring: Case Study in the Marche Region (Italy)

Abstract. Augmented Reality (AR) allows the real world to be enhanced through the overlapping of digital information generated by an algorithm or software amplifying the user's perception. In this research, the potential of AR is exploited by applying it to the problem of air pollution visualization to raise awareness of the issue. It is well known that the principal sources of air pollution include industry, domestic heating, traffic, production processes and more others. According to European Environment Agency, air pollution is the largest environmental health risk in Europe in 2022 affecting respiratory and cardiovascular diseases and premature deaths. A suitable combination of GIS-based procedures for data analysis and georeferencing, AR systems for the visualization and exploitation of the results, can be a strategic driver. The research presented in this paper has been experimented in the Marche Region, in the central part of Italy, and in particular the city of Ancona. The pollutant data collected come from 17 Agenzia Regionale per la Protezione Ambientale delle Marche (ARPAM) meteorological ground-based stations located in Marche Region. The data processing have been executed using the software ArcGis Pro instead of a 3D City Model has been acquired from the online 3D ArcGIS platform. According to the Dlgs.155/2010 regulation, the AQI index (Air Quality Index) has been provided by the interpolation of the meteorological ground stations data. The visualization phase has been tackled exploiting in a WebGIS platform and an APP by AR. A 3D immersive visualization is made using five AQI level of air quality (excellent, good, fair, poor, bad) in order to increase the urban sensing.

Blind Recognition of Frame Synchronization Based on Deep Learning.

In this paper, a deep-learning-based frame synchronization blind recognition algorithm is proposed to improve the detection performance in non-cooperative communication systems. Current methods face challenges in accurately detecting frames under high bit error rates (BER). Our approach begins with flat-top interpolation of binary data and converting it into a series of grayscale images, enabling the application of image processing techniques. By incorporating a scaling factor, we generate RGB images. Based on the matching radius, frame length, and frame synchronization code, RGB images with distinct stripe features are classified as positive samples for each category, while the remaining images are classified as negative samples. Finally, the neural network is trained on these sets to classify test data effectively. Simulation results demonstrate that the proposed algorithm achieves a 100% probability in frame recognition when BER is below 0.2. Even with a BER of 0.25, the recognition probability remains above 90%, which exhibits a performance improvement of over 60% compared with traditional algorithms. This work addresses the shortcomings of existing methods under high error conditions, and the idea of converting sequences into RGB images also provides a reliable solution for frame synchronization in challenging communication environments.

A hybrid sparrow optimization Kriging model and its application in geological modeling

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

Machine Learning-Based Soil Moisture Retrieval Using CYGNSS and Interpolated SMAP Data

Abstract. Soil moisture is a crucial component of the global terrestrial ecosystem water vapor cycle, and higher spatial-temporal soil moisture product has significant impacts on research in agriculture, hydrology, and ecology. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology which can be used to retrieve soil moisture. Cyclone Global Navigation Satellite System (CYGNSS), launched by NASA in 2016 to observe ocean surface hurricanes, has also collected a substantial amount of data of GNSS signals reflected over the land. This paper focuses on soil moisture retrieval using CYGNSS and SMAP dataset. In this study, three machine learning methods (GSVM, BP and RF) are used for spatial interpolation of SMAP soil moisture data. Result shows RF interpolation is the best with the RMSE of 0.044 cm3/cm3 and the CC of 0.959 compared with SMAP truly SM. Then soil moisture data after RF interpolation and conventional linear interpolation are respectively used as response variables to train the retrieval model by ELM, BP and RF machine learning methods respectively. Results show that, among the six regression models, the RF-RF regression model performs the best with the RMSE of 0.012 cm3/cm3 and the CC of 0.986. Using this model, we get a global soil moisture product and have higher temporal and spatial resolution.

Determination of emissivity profiles using a Bayesian data-driven approach

In this paper, we explore the determination of a spectral emissivity profile that closely matches real data, intended for use as an initial guess and/or a priori information in a retrieval code. Our approach employs a Bayesian method that integrates the CAMEL (Combined ASTER MODIS Emissivity over Land) emissivity database with the MODIS/Terra＋Aqua Yearly Land Cover Type database. The solution is derived as a convex combination of high-resolution Huang profiles using the Bayesian framework. We test our method on IASI (Infrared Atmospheric Sounding Interferometer) data and find that it outperforms the linear spline interpolation of the CAMEL data and the Huang emissivity database itself.

FEATURES OF RESTORING MEASUREMENT VALUES OF THE DOPPLER RADAR SYSTEM MFTR-2100/40 DURING ARMAMENT AND MILITARY EQUIPMENT TESTING

During the registration and processing of measurement information obtained by the MFTR-2100/40 radar system, there is a probability of missing values in the trajectory measurement data. Experience with the radar system indicates a high sensitivity of the angular channels to the propagation of the radar signal in the real atmosphere. As a result of these errors, object tracking may be lost over a certain period, causing periodic delays in data registration, necessitating the filling or restoring of missing values with acceptable accuracy. The built-in algorithms and mathematical methods of the WinTrack specialized software (SS) allow for the interpolation and extrapolation of the trajectory measurement data of the MFTR-2100/40 radar system for visual display and plotting of tracks, including ranges with missing values. However, the functional capabilities of the WinTrack SS are not designed for full and accurate restoration of missing values. Additionally, the limited tools of the WinTrack SS necessitate finding alternatives in other software to ensure the reliability of the restored results. Trajectory measurement data is a large dataset, so selecting the right method for filling in missing values is crucial to maintaining a low percentage of deviations in the obtained results. This publication examines the functional capabilities of the WinTrack SS that allow for the restoration of missing trajectory measurement values. The approximation of measured trajectory data values in the WinTrack SS enables partial filling of missing values upon export, but not for all data types. An algorithm for the process of restoring missing values in trajectory measurement data arrays is provided.

Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area

Residual biomass from agricultural production and processing, such as citrus pulp and olive pomace, is an important resource for energy production. In particular, this is the case in regions where transformation industries are concentrated.Current biomass estimates often focus on actual production data, that may not fully capture the biomass potential across all suitable cultivation areas. To bridge this gap, the study predicts the overall potential biomass available for energy production, taking into account the total area suitable for citrus cultivation.The research is focused on the study of citrus species in the province of Syracuse, Sicily, Italy. The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. The results highlighted potential citrus cultivation on 47,706 ha and estimated 184,340 t of biomass, 16,461,520.82 Nm3 of biogas, and 8110 t of digestate. The results of the study identified potential areas for both increasing biomass production and optimising the distribution of digestate, thus demonstrating the utility of these thematic maps as a decision support tool for land management. The simulations and the methodology applied in this study indicated potential economic and environmental benefits to be gained from sustainable by-product management. This approach facilitates the optimisation of decision-making processes for land planning, thereby contributing to the broader objective of improving resource efficiency and sustainability in the agricultural production and processing sectors.