102 citations as recorded by crossref.

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2. Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area B. Pan et al. 10.3390/app13169321
3. Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning W. Tian et al. 10.1016/j.saa.2024.124396
4. Application of mid-infrared (MIR) spectroscopy to identify and quantify minerals in New Zealand soils Y. Ma et al. 10.1016/j.catena.2024.108115
5. Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model X. Zhang et al. 10.3390/rs16030565
6. A Review of Machine Learning for Near-Infrared Spectroscopy W. Zhang et al. 10.3390/s22249764
7. Advancing Soil Organic Carbon and Total Nitrogen Modelling in Peatlands: The Impact of Environmental Variable Resolution and vis-NIR Spectroscopy Integration W. Mendes & M. Sommer 10.3390/agronomy13071800
8. Label‐Free Blood Typing by Raman Spectroscopy and Artificial Intelligence E. Jensen et al. 10.1002/admt.202301462
9. FADC-VIS: A novel ore classification method based on near-infrared visible spectrum and contrast-enhanced deep network J. Zou et al. 10.1016/j.microc.2024.112229
10. Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development Y. Pang et al. 10.1016/j.egyai.2024.100428
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12. Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application B. Hu et al. 10.1016/j.geoderma.2024.116798
13. A novel method for predicting the geochemical composition of tailings with laboratory field and hyperspectral airborne data using a regression and classification-based approach Y. Ogen et al. 10.1080/22797254.2022.2104173
14. A critical systematic review on spectral-based soil nutrient prediction using machine learning S. Jain et al. 10.1007/s10661-024-12817-6
15. Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer A. Nofrizal et al. 10.3390/rs14091997
16. A global soil spectral calibration library and estimation service K. Shepherd et al. 10.1016/j.soisec.2022.100061
17. Improving the performance of a spectral model to estimate total nitrogen content with small soil samples sizes W. Tang et al. 10.1186/s40538-024-00552-6
18. Analysis of the Composition of Different Instars of Tenebrio molitor Larvae using Near-Infrared Reflectance Spectroscopy for Prediction of Amino and Fatty Acid Content N. Kröncke et al. 10.3390/insects14040310
19. Quantitative analysis of near infrared spectroscopic data based on dual-band transformation and competitive adaptive reweighted sampling Y. Li & X. Yang 10.1016/j.saa.2022.121924
20. The state-of-the-art use of X-Ray and infrared for evaluating soil attributes J. Arruda-Oliveira et al. 10.54033/cadpedv21n3-182
21. Orchard classification based on super-pixels and deep learning with sparse optical images J. Li et al. 10.1016/j.compag.2023.108379
22. Hand-feel soil texture classes and particle-size distribution as predictors of soil water content at field capacity. Further insights into the sources of uncertainty A. Richer-de-Forges et al. 10.1016/j.catena.2024.108268
23. Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response S. Gruszczyński & W. Gruszczyński 10.1016/j.geoderma.2021.115451
24. A Convolutional Neural Network for Steady-State Flow Approximation Trained on a Small Sample Size G. Zhong et al. 10.3390/atmos14091462
25. Strategies and methods for predicting soil organic matter at the field scale based on the provincial near infrared spectral database S. Jia et al. 10.17221/133/2022-SWR
26. Mango variety classification based on convolutional neural network with attention mechanism and near-infrared spectroscopy Z. Dong et al. 10.1007/s11694-023-02320-w
27. Estimation of grain quality parameters in rice for high‐throughput screening with near‐infrared spectroscopy and deep learning P. Ravichandran et al. 10.1002/cche.10546
28. Using deep neural networks for evaluation of soil quality based on VIS–NIR spectroscopy M. Safaie et al. 10.1007/s12145-023-01168-4
29. Inversion of mine ventilation resistance coefficients enhanced by deep reinforcement learning P. Cao et al. 10.1016/j.psep.2023.12.005
30. Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms M. Rocha et al. 10.1016/j.bbe.2024.10.001
31. New approach for rapid estimation of leaf nitrogen, phosphorus, and potassium contents in apple-trees using Vis/NIR spectroscopy based on wavelength selection coupled with machine learning R. Azadnia et al. 10.1016/j.compag.2023.107746
32. Microstructural, mechanical, tribological, and corrosion behavior of ultrafine bio-degradable Mg/CeO2 nanocomposites: Machine learning-based modeling and experiment S. Deka et al. 10.1016/j.triboint.2023.109063
33. Estimation of soil texture by fusion of near-infrared spectroscopy and image data based on convolutional neural network M. Vakilzadeh Ebrahimi et al. 10.1016/j.compag.2023.108117
34. High-resolution mapping of soil carbon stocks in the western Amazon C. Moquedace et al. 10.1016/j.geodrs.2024.e00773
35. Quantitative Analysis of Near-Infrared Spectroscopy of Blended Fabrics Based on Convolutional Neural Network Y. Tao 10.1109/ACCESS.2023.3275430
36. Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library D. Zhang et al. 10.3390/acoustics6030037
37. Mapping topsoil pH using different predictive models and covariate sets in Henan Province, Central China L. Zhang et al. 10.1016/j.ecoinf.2023.102290
38. On-site soil analysis: A novel approach combining NIR spectroscopy, remote sensing and deep learning M. Kok et al. 10.1016/j.geoderma.2024.116903
39. Improvement of accuracy and resilience in FHR classification via double trend accumulation encoding and attention mechanism Z. Zhou et al. 10.1016/j.bspc.2023.104929
40. High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
41. A Study on Sample Size Sensitivity of Factory Manufacturing Dataset for CNN-Based Defective Product Classification D. Kim et al. 10.3390/computation10080142
42. Predicting the particle size distribution of fine-grained and sandy soils using deep learning for classifying recovered soils separated from tsunami deposits M. Iwashita et al. 10.1007/s10163-022-01404-x
43. Fresh Leaf Spectroscopy to Estimate the Crop Nutrient Status of Potato (Solanum tuberosum L.) A. Sharma et al. 10.1007/s11540-024-09766-5
44. Developing Vis–NIR libraries to predict cation exchange capacity (CEC) and pH in Australian sugarcane soil X. Zhao et al. 10.1016/j.compag.2024.109004
45. Estimating scattering potentials in inverse problems with Volterra series and neural networks G. Balassa 10.1140/epja/s10050-022-00839-y
46. A new method for high-precision estimation of soil organic matter using two-dimensional correlation spectroscopy—to support collaborative use of global open soil spectral libraries K. Xia et al. 10.1016/j.geoderma.2024.116877
47. Sample Size Optimization for Digital Soil Mapping: An Empirical Example D. Saurette et al. 10.3390/land13030365
48. Predicting soil properties for fertiliser recommendation in South Korea using MIR spectroscopy S. Jeon et al. 10.1016/j.geodrs.2024.e00901
49. Improving spectral estimation of soil inorganic carbon in urban and suburban areas by coupling continuous wavelet transform with geographical stratification Y. Hong et al. 10.1016/j.geoderma.2022.116284
50. Near infrared spectroscopy to rapid assess the rubber tree clone and the influence of maturation and disease at the leaves A. Lucas Domingos da Silva et al. 10.1016/j.microc.2021.106478
51. Predicting the Mathematics Literacy of Resilient Students from High‐performing Economies: A Machine Learning Approach Y. Zhang & M. Cutumisu 10.1016/j.stueduc.2024.101412
52. A fair evaluation of the potential of machine learning in maritime transportation X. Luo et al. 10.3934/era.2023243
53. The area prediction of western North Pacific Subtropical High in summer based on Gaussian Naive Bayes D. Li et al. 10.1007/s00382-022-06252-x
54. Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition N. Kröncke et al. 10.3390/insects14020114
55. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al. 10.1016/j.scitotenv.2023.169647
56. Using Mid-Infrared Spectroscopy to Optimize Throughput and Costs of Soil Organic Carbon and Nitrogen Estimates: An Assessment in Grassland Soils P. Ramírez et al. 10.3390/environments9120149
57. Comparative Analysis of Lignocellulose Agricultural Waste and Pre-treatment Conditions with FTIR and Machine Learning Modeling M. Pancholi et al. 10.1007/s12155-022-10444-y
58. A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics F. Hussain et al. 10.1038/s41598-024-54391-4
59. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign D. Jiang et al. 10.3390/s24041245
60. Uncertainty quantification for structural response field with ultra-high dimensions L. Cao & Y. Zhao 10.1016/j.ijmecsci.2024.109110
61. Assessing the uncertainty of deep learning soil spectral models using Monte Carlo dropout J. Padarian et al. 10.1016/j.geoderma.2022.116063
62. Prediction of Soil Erodibility by Diffuse Reflectance Spectroscopy in a Neotropical Dry Forest Biome S. Pontes et al. 10.3390/land11122188
63. Impact of Land Cover Changes on Soil Type Mapping in Plain Areas: Evidence from Tongzhou District of Beijing, China X. Wu et al. 10.3390/land12091696
64. Density of soil observations in digital soil mapping: A study in the Mayenne region, France T. Loiseau et al. 10.1016/j.geodrs.2021.e00358
65. Real-time detection of ammonium in soil pore water R. Yupiter et al. 10.1038/s41545-023-00243-z
66. Improving the Performance of Automated Rooftop Extraction through Geospatial Stratified and Optimized Sampling Z. Sun et al. 10.3390/rs14194961
67. Data mining of urban soil spectral library for estimating organic carbon Y. Hong et al. 10.1016/j.geoderma.2022.116102
68. Non-linear memory-based learning for predicting soil properties using a regional vis-NIR spectral library Z. Wang et al. 10.1016/j.geoderma.2023.116752
69. A CNN model for predicting soil properties using VIS–NIR spectral data M. Hosseinpour-Zarnaq et al. 10.1007/s12665-023-11073-0
70. Soil Surface Texture Classification Using RGB Images Acquired Under Uncontrolled Field Conditions E. Babalola et al. 10.1109/ACCESS.2023.3290907
71. An innovative variant based on generative adversarial network (GAN): Regression GAN combined with hyperspectral imaging to predict pesticide residue content of Hami melon H. Tan et al. 10.1016/j.saa.2024.125086
72. Based on the Role of Intelligent Multimedia Man-Machine Exchange in Product Design F. Su & C. Wu 10.1155/2022/5836965
73. Improving prediction model robustness with virtual sample construction for near-infrared spectra analysis Y. Hao et al. 10.1016/j.aca.2023.341763
74. Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications J. Nagel‐Held et al. 10.1002/cche.10732
75. Quantifying uncertainty in the prediction of soil properties using mid-infrared spectra O. Omondiagbe et al. 10.1016/j.geoderma.2024.116954
76. AI-assisted food enzymes design and engineering: a critical review X. Wang et al. 10.1007/s43393-022-00138-z
77. Development of a near‐infrared spectroscopy calibration for Hagberg falling number assessment of barley (Hordeum vulgare): A comparison of methods N. Gerisch et al. 10.1111/pbr.13024
78. Review: The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation. II. The rise of convolutional neural networks J. Walsh et al. 10.1177/09670335231173140
79. Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences H. Rafiq et al. 10.3390/plants13060833
80. Applications of near infrared spectroscopy and hyperspectral imaging techniques in anaerobic digestion of bio-wastes: A review W. Peng et al. 10.1016/j.rser.2022.112608
81. Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation Y. Hong et al. 10.1016/j.catena.2023.107628
82. Peatlands spectral data influence in global spectral modelling of soil organic carbon and total nitrogen using visible-near-infrared spectroscopy W. Mendes et al. 10.1016/j.jenvman.2022.115383
83. Predicting microbial responses to changes in soil physical and chemical properties under different land management S. Sadeghi et al. 10.1016/j.apsoil.2023.104878
84. Review of deep learning-based methods for non-destructive evaluation of agricultural products Z. Li et al. 10.1016/j.biosystemseng.2024.07.002
85. Deep Learning-Based Cervical Spine Posterior Percutaneous Endoscopic Disc Nucleus Resection for the Treatment of Cervical Spondylotic Radiculopathy Y. Zhang et al. 10.1155/2021/7245566
86. Geometry and boundary condition adaptive data-driven model of fluid flow based on deep convolutional neural networks J. Peng et al. 10.1063/5.0073419
87. Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library X. Zhang et al. 10.3390/rs15020465
88. Soil texture prediction with automated deep convolutional neural networks and population-based learning O. Omondiagbe et al. 10.1016/j.geoderma.2023.116521
89. Generating realistic infrared spectra using artificial neural networks L. Győry et al. 10.1002/cem.3573
90. Simultaneous estimation of multiple soil properties under moist conditions using fractional-order derivative of vis-NIR spectra and deep learning Y. Liu et al. 10.1016/j.geoderma.2023.116653
91. Unravelling spatial drivers of topsoil total carbon variability in tropical paddy soils of Sri Lanka T. Paranavithana et al. 10.1016/j.geodrs.2023.e00745
92. Deep learning for near-infrared spectral data modelling: Hypes and benefits P. Mishra et al. 10.1016/j.trac.2022.116804
93. Temperature interpretation method for temperature indicating paint based on spectrogram J. Ge et al. 10.1016/j.measurement.2023.113317
94. Deep Learning‐Assisted Design of Novel Promoters in Escherichia coli X. Wang et al. 10.1002/ggn2.202300184
95. Zero-day attack detection: a systematic literature review R. Ahmad et al. 10.1007/s10462-023-10437-z
96. Data fusion and network intrusion detection systems R. Ahmad & I. Alsmadi 10.1007/s10586-024-04365-y
97. Towards building data analytics benchmarks for IoT intrusion detection R. Ahmad et al. 10.1007/s10586-021-03388-z
98. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
99. Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response S. Gruszczyński & W. Gruszczyński 10.1016/j.geoderma.2021.115451
100. Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar K. Kawamura et al. 10.3390/rs13081519
101. The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A Laboratory Simulation Study F. Zhang et al. 10.3390/rs14020397
102. Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau C. Jiang et al. 10.3390/s23073686