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Advanced Modeling Approaches for Quality Assessment of Papaya Leather Dried in IoT-Enabled IR-Assisted Refractance Window Dryer

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JOURNAL OF FOOD PROCESS ENGINEERING 2025年第2期48卷

作者： Dadhaneeya, Harsh Nema, Prabhat K. Warepam, Sophia Chanu Natl Inst Food Technol Entrepreneurship & Manageme Dept Food Engn Sonipat Haryana India

The aim of this research is the comprehensive comparison of advanced modeling tools for predicting the quality parameters of IoT-enabled IR-assisted RW-dried papaya leather. The models used in the current research were "response surface methodology (RSM)," "artificial neural network (ANN)," "machine learning regression algorithms (MLRA)," and "adaptive neuro-fuzzy inference system (ANFIS)." This comprehensive compression could be differentiated based on the model's fitness and prediction capabilities. The fitness of the model was assessed using statistical metrics such as "root mean square error (RMSE)," "mean square error (MAE)," and "coefficient of determination (R-2)." While the prediction capability was evaluated by using tactics like predicting error, predicting accuracy, chi-square, and associated p-values. The results indicated that both the RSM and MLRA models had substantial predictive capabilities and achieved a prediction accuracy of 98.992 and 97.169, respectively. This study concluded that the model's fitness was getting excellent in the Alpha ANN (R-2 = 0.9943) and ANFIS (R-2 = 0.9903) models. However, when evaluating the prediction capabilities, the RSM and MLRA models outperformed the others.

关键词： adaptive neuro-fuzzy inference system artificial neural network IoT-enabled IR-assisted refractance window dryer machine learning regression algorithms papaya leather

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Prediction of Crop Yield with Feature Dimension Reduction Method Using regression Models 7

Prediction of Crop Yield with Feature Dimension Reduction Me...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Raddekar, Ashwini Chikmurge, Diptee Subhedar, Mansi School of Computer Engineering Mit Academy of Engineering Department of Computer Engineering Maharashtra Alandi India Pillai Hoc College of Engineering and Technology Department of Electronics and Telecommunication Engineering Tal. Khalapur Dit. Raigad Maharashtra Rasayani India

ISBN: (纸本)9798350304268

The important factor in the development of the Indian economy is agriculture. Agricultural production supports more than 70% of rural households. Approximately 17% of the GDP of India is contributed by the agricultural sector, making it a significant economic sector. The main aim of the proposed method is to predict crop yield and analyze the best model to predict the yield of crops among various machine learning algorithms, This model can certainly help farmers to take suitable decisions to grow crops and improve the Indian economy. The dataset employed here includes various parameters such as the amount of fertilizer, the area planted, harvested area, etc. In this work lint crop dataset is considered for crop yield prediction. In the initial step, Principal Component Analysis (PCA) is used for the feature dimension reduction without losing important information/context of the data. It achieves this by reducing the number of dimensions in the data. After reducing the dimension of the dataset using PCA, various machine learning regression algorithms such as Linear rearcssion, Lasso regression, Decision tree, Adaboost, and XGBoost are employed to predict the crop yield. Further, the best model is found by comparing the model's performance metrics which include Mean absolute error (MAE), R2 Score, and Root mean square error (RMSE). © 2023 IEEE.

关键词： crop prediction machine learning regression algorithms principal component analysis

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Using crack width for shear, stiffness, and stirrup strain history predictions for reinforced concrete beams

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2024年

作者： Castillo, Rodrigo Elhami-Khorasani, Negar Okumus, Pinar Chandola, Varun Univ Buffalo Dept Civil Struct & Environm Engn Buffalo NY 14260 USA Univ Buffalo Dept Comp Sci & Engn Buffalo NY USA

Shear failures in reinforced concrete structures occur with little or no warning. Reinforced concrete members with shear cracks should be evaluated to ensure safety. Existing evaluation methods have large variability, require time-consuming modeling or expert opinion. This study uses machine learning to investigate correlations of crack width with shear loading, stiffness, and stirrup strain histories. Experimental literature enables the assembly of a database of rectangular reinforced concrete slender beams with crack width measurements for beams with shear reinforcement amounts smaller and larger than the minimum required by ACI 318-19. Measured data include crack widths from 122 beams, load-displacement relationship from 100 beams, and stirrup strains from 46 beams. Gaussian Process regression is used to correlate crack width and geometric, material and design properties to shear loading, stiffness, and stirrup strain histories. Ten-fold cross validation training shows mean absolute percent errors of 18, 33 and 77% for shear, stiffness, and stirrup strain history predictions. The proposed algorithms can be used to accelerate the evaluation of in-service structures and can be updated upon availability of additional data.

关键词： Concrete beams Gaussian process machine learning regression algorithms shear cracks shear evaluation shear stiffness stirrup strain

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Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

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EUROPEAN JOURNAL OF REMOTE SENSING 2023年第1期56卷

作者： Ranghetti, Marina Boschetti, Mirco Ranghetti, Luigi Tagliabue, Giulia Panigada, Cinzia Gianinetto, Marco Verrelst, Jochem Candiani, Gabriele Natl Res Council Italy Inst Electromagnet Sensing Environm IREA Via Corti 12 I-20133 Milan Italy Univ Milano Bicocca Remote Sensing Environm Dynam Lab Dipartimento Sci Ambiente & Terra Milan Italy Dept Architecture Built Environm & Construct Engn Milan Italy Univ Valencia Image Proc Lab IPL Parc Cient Valencia Spain

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R-2 = 0.82 and R-2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.

关键词： Precision farming radiative transfer models machine learning regression algorithms plant nitrogen uptake estimation

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Biophysical Variable Retrieval of Silage Maize with Gaussian Process regression and Hyperparameter Optimization algorithms

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REMOTE SENSING 2023年第14期15卷 3690-3690页

作者： Akbari, Elahe Boloorani, Ali Darvishi Verrelst, Jochem Pignatti, Stefano Samany, Najmeh Neysani Soufizadeh, Saeid Hamzeh, Saeid Hakim Sabzevari Univ Fac Geog & Environm Sci Dept Remote Sensing & Geog Informat Syst Sabzevar *** Iran Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran 141785393 Iran Univ Valencia Image Proc Lab IPL C-Catedrat Jose Beltran 2 Paterna 46980 Valencia Spain Inst Methodol Environm Anal CNR IMAA I-85050 Tito PZ Italy Shahid Beheshti Univ Environm Sci Res Inst Dept Agroecol Tehran *** Iran

Quantification of vegetation biophysical variables such as leaf area index (LAI), fractional vegetation cover (fCover), and biomass are among the key factors across hydrological, agricultural, and irrigation management studies. The present study proposes a kernel-based machine learning algorithm capable of performing adaptive and nonlinear data fitting so as to generate a suitable, accurate, and robust algorithm for spatio-temporal estimation of the three mentioned variables using Sentinel-2 images. To this aim, Gaussian process regression (GPR)-particle swarm optimization (PSO), GPR-genetic algorithm (GA), GPR-tabu search (TS), and GPR-simulated annealing (SA) hyperparameter-optimized algorithms were developed and compared against kernel-based machine learning regression algorithms and artificial neural network (ANN) and random forest (RF) algorithms. The accuracy of the proposed algorithms was assessed using digital hemispherical photography (DHP) data and destructive measurements performed during the growing season of silage maize in agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019. The results on biophysical variables against validation data showed that the developed GPR-PSO algorithm outperformed other algorithms under study in terms of robustness and accuracy (0.917, 0.931, 0.882 using R-2 and 0.627, 0.078, and 1.99 using RMSE in LAI, fCover, and biomass of Sentinel-2 20 m, respectively). GPR-PSO also possesses the unique ability to generate pixel-based uncertainty maps (confidence level) for prediction purposes (i.e., estimated uncertainty level <0.7 in LAI, fCover, and biomass, for 96%, 98%, and 71% of the total study area, respectively). Altogether, GPR-PSO appears to be the most suitable option for mapping biophysical variables at the local scale using Sentinel-2 images.

关键词： biophysical variables optimization algorithm Gaussian process regression machine learning regression algorithms remote sensing

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Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images 23

Estimation of leaf area index at the late growth stage of cr...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII

作者： Kong, Weiping Huang, Wenjiang Ma, Lingling Chen, Binbin Li, Chuanrong Tang, Lingli Chinese Acad Sci Aerosp Informat Res Inst Key Lab Quantitat Remote Sensing Informat Technol Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China

ISBN: (数字)9781510645578

ISBN: (纸本)9781510645578;9781510645561

Estimation of leaf area index (LAI) is of vital importance to improve the prediction accuracy of crops quality and yield. However, it is more difficult to precisely assess LAI at the late growth stages of crops due to the influences of leaf senescence and soil background. Unmanned aerial vehicles (UAVs), with hyperspectral sensors onboard, can acquire high spatial and spectral resolution images and provide detailed information of fields, and consequently, are widely used for monitoring the biophysical parameters of crops in precision agriculture. The aim of this study was to evaluate the potential of UAV-based hyperspectral data in LAI estimation for sunflower and maize at the milk-filling stage, with machine learning regression algorithms (MLRA) for data analyses. Three algorithms including linear regression (LR), partial least square regression (PLSR) and kernel ridge regression (KRR) were used with the individual vegetation index (VI), VI-combination and spectral reflectance of full wavelengths as input variables. Results indicate that from the perspective of accuracy of estimation models, the PLSR based on VI-combination derived from hyperspectral images outperformed the LR based on individual VI and KRR based on VI-combination or spectral reflectance, which was proven to be the most suitable for the LAI estimation for both maize and sunflower at late growth stage, with 68% and 64% of the variation in LAI were explained, respectively. From the perspective of VIs tested, the modified triangular vegetation index (MTVI1) and improved soil-adjusted vegetation index (MSAVI) were found to be the best LAI estimators for maize and sunflower. Meanwhile, the contributions of the two VIs were also superior over other VIs tested in developing estimation models based on the PLSR method.

关键词： LAI UAV hyperspectral sensor machine learning regression algorithms crops late growth stage

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Application of Kalman filtering technique for evaluation of neutron cross section data of ¹⁰⁰Mo (n, 2n) ⁹⁹Mo reaction

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2021年 1020卷 165850-165850页

作者： Ram, Sangeetha Prasanna Nair, Jayalekshmi Suryanarayana, Saraswatula Venkata Ganesan, S. VES Inst Technol Dept Instrumentat Mumbai 400074 Maharashtra India Bhabha Atom Res Ctr Nucl Phys Div Mumbai 400085 Maharashtra India Bhabha Atom Res Ctr Reactor Phys Design Div Reactor Design & Dev Grp Mumbai 400085 Maharashtra India

In this paper, for the first time, evaluation of neutron cross section data of 100Mo (n, 2n) 99Mo reaction is performed using experimental data available in IAEA-EXFOR database library and nuclear model-based data generated using Talys 1.9 code by applying a novel method of combining Kalman filtering technique with machine learning (ML) regression algorithms. The neutron cross section data evaluation has been performed after a detailed study of all the EXFOR papers corresponding to 100Mo (n, 2n) 99Mo reaction and nuclear model based cross section generation by executing T6 random input files using Talys 1.9 code. The evaluated curve generated is then compared with the existing evaluated curve of 100Mo (n, 2n) 99Mo reaction from nuclear data libraries such as ENDF/B-VIII.0, JEFF-3.3, JENDL-4.0, CENDL-3.1 and TENDL 2017 and found to be in good agreement with them. Chi-square and generalized Chi-square tests were employed to assess the proposed evaluation techniques and found them to be good in estimating evaluated mean values and evaluated uncertainties of cross section.

关键词： Nuclear data evaluation Kalman filtering technique machine learning regression algorithms Covariance analysis

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Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data

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REMOTE SENSING OF ENVIRONMENT 2018年 204卷 366-379页

作者： Zhang, Caiyun Denka, Sara Cooper, Hannah Mishra, Deepak R. Florida Atlantic Univ Dept Geosci 777 Glades Rd Boca Raton FL 33431 USA Univ Georgia Ctr Geospatial Res Dept Geog 210 Field St Athens GA 30602 USA

Large-scale biomass quantification of sawgrass (Cladium jamaicense) marsh is critical to understand the carbon and energy cycle in the Florida Everglades. There is also a need to monitor biomass changes in the coastal Everglades due to continuing sea level rise. Previous research in biomass estimation of coastal marshes has focused on pixel-based parametric modeling methods. In this study, an object-based ensemble analysis approach was developed to map sawgrass biomass at multiple scales using Landsat data. Four machine learning regression algorithms including Support Vector machine (SVM), Random Forest (RF), k-Nearest Neighbor (k-NN), and Artificial Neural Network (ANN) were evaluated and compared to the commonly used Multiple Linear regression (MLR) method for both live and total sawgrass biomass estimation. A weighted combining scheme was developed to integrate predictions from comparable models for ensemble analysis. Nonparametric machine learning models had better performance than the parametric approach. ANN and SVM produced similar results in live biomass estimation with the correlation coefficient (r) larger than 0.9, while ANN achieved the best result for the total biomass estimation (r = 0.94). Sawgrass biomass maps were produced for two harvest seasons in 2014 and 2016 at three detail levels, which successfully revealed the spatial and temporal (seasonal and interannual) sawgrass biomass variations. Ensemble analysis of the ANN and SVM predictions of live sawgrass biomass not only made the estimation more reliable, but also generated an uncertainty map to identify the regions with a robust biomass prediction, as well as challenging areas for biomass quantification. It is concluded that the object-based ensemble analysis is a promising alternative to the commonly used pixel-based biomass modeling techniques.

关键词： Object-based biomass modeling Ensemble analysis for biomass prediction machine learning regression algorithms Coastal marshes

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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

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REMOTE SENSING 2018年第12期10卷 1942-1942页

作者： Mananze, Sosdito Pocas, Isabel Cunha, Mario Univ Eduardo Mondlane Escola Super Desenvolvimento Rural Vilankulo PC 257 Mozambique Univ Porto Fac Ciencias Rua Campo Alegre S-N P-4169007 Porto Portugal Geospace Sci Res Ctr Rua Campo Alegre S-N P-4169007 Porto Portugal Univ Lisbon Inst Super Agron Linking Landscape Environm Agr & Food LEAF P-1349017 Lisbon Portugal Univ Porto Campus Fac Engn Inst Syst & Comp Engn Technol & Sci INESC TEC Rua Dr Roberto Frias P-4200465 Porto Portugal

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDI(d: 725;715;565)) for the hyperspectral dataset and the modified simple ratio (mSR(c: 740;705;865)) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665;865;783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

关键词： field-spectroradiometer Sentinel-2 hyperspectral multispectral leaf area index vegetation indices machine learning regression algorithms PROSAIL LUT inversion

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Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 132卷 88-101页

作者： Pablo Rivera-Caicedo, Juan Verrelst, Jochem Munoz-Mari, Jordi Camps-Valls, Gustau Moreno, Jose Univ Valencia IPL Parc Cient Paterna 46980 Spain CONACYT UAN Secretary Res & Postgrad Tepic 63155 Nayarit Mexico

Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to retrieve leaf area index (LAI): (1) a simulated PROSAIL reflectance data (2101 bands), and (2) a field dataset from airborne HyMap data (125 bands). For the majority of MLRAs, applying first a DR method leads to superior retrieval accuracies and substantial gains in processing speed as opposed to using all bands into the regression algorithm. This was especially noticeable for the PROSAIL dataset: in the most extreme case, using the classical linear regression (LR), validation results R-CV(2) (RMSECV) improved from 0.06 (12.23) without a DR method to 0.93 (0.53) when combining it with a best performing DR method (i.e., CCA or OPLS). However, these DR methods no longer excelled when applied to noisy or real sensor data such as HyMap. Then the combination of kernel CCA (KCCA) with LR, or a classical PCA and PLS with a MLRA showed more robust performances (R-CV(2) of 0.93). Gaussian processes regression (GPR) uncertainty estimates revealed that LAI maps as trained in combination with a DR method can lead to lower uncertainties, as opposed to using all HyMap bands. The obtained results demonstrated that, in general, biophysical variable retrieval from hyperspectral data can largely benefit from dimensionality reduction in both accuracy and computational efficiency. (C) 2017 International Society

关键词： Spectral dimensionality reduction methods machine learning regression algorithms Biophysical parameter retrieval ARTMO Hyperspectral Vegetation properties

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