86 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. A fair evaluation of the potential of machine learning in maritime transportation X. Luo et al. 10.3934/era.2023243
4. 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
5. 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
6. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al. 10.1016/j.scitotenv.2023.169647
7. 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
8. Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model X. Zhang et al. 10.3390/rs16030565
9. A Review of Machine Learning for Near-Infrared Spectroscopy W. Zhang et al. 10.3390/s22249764
10. 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
11. 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
12. Label‐Free Blood Typing by Raman Spectroscopy and Artificial Intelligence E. Jensen et al. 10.1002/admt.202301462
13. 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
14. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign D. Jiang et al. 10.3390/s24041245
15. Uncertainty quantification for structural response field with ultra-high dimensions L. Cao & Y. Zhao 10.1016/j.ijmecsci.2024.109110
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18. Fine-resolution mapping of cropland topsoil pH of Southern China and its environmental application B. Hu et al. 10.1016/j.geoderma.2024.116798
19. 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
20. Prediction of Soil Erodibility by Diffuse Reflectance Spectroscopy in a Neotropical Dry Forest Biome S. Pontes et al. 10.3390/land11122188
21. Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer A. Nofrizal et al. 10.3390/rs14091997
22. A global soil spectral calibration library and estimation service K. Shepherd et al. 10.1016/j.soisec.2022.100061
23. 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
24. 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
25. 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
26. Real-time detection of ammonium in soil pore water R. Yupiter et al. 10.1038/s41545-023-00243-z
27. Improving the Performance of Automated Rooftop Extraction through Geospatial Stratified and Optimized Sampling Z. Sun et al. 10.3390/rs14194961
28. 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
29. 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
30. Data mining of urban soil spectral library for estimating organic carbon Y. Hong et al. 10.1016/j.geoderma.2022.116102
31. 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
32. The state-of-the-art use of X-Ray and infrared for evaluating soil attributes J. Arruda-Oliveira et al. 10.54033/cadpedv21n3-182
33. A CNN model for predicting soil properties using VIS–NIR spectral data M. Hosseinpour-Zarnaq et al. 10.1007/s12665-023-11073-0
34. Soil Surface Texture Classification Using RGB Images Acquired Under Uncontrolled Field Conditions E. Babalola et al. 10.1109/ACCESS.2023.3290907
35. Orchard classification based on super-pixels and deep learning with sparse optical images J. Li et al. 10.1016/j.compag.2023.108379
36. Based on the Role of Intelligent Multimedia Man-Machine Exchange in Product Design F. Su & C. Wu 10.1155/2022/5836965
37. Improving prediction model robustness with virtual sample construction for near-infrared spectra analysis Y. Hao et al. 10.1016/j.aca.2023.341763
38. 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
39. Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications J. Nagel‐Held et al. 10.1002/cche.10732
40. A Convolutional Neural Network for Steady-State Flow Approximation Trained on a Small Sample Size G. Zhong et al. 10.3390/atmos14091462
41. 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
42. 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
43. 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
44. AI-assisted food enzymes design and engineering: a critical review X. Wang et al. 10.1007/s43393-022-00138-z
45. Using deep neural networks for evaluation of soil quality based on VIS–NIR spectroscopy M. Safaie et al. 10.1007/s12145-023-01168-4
46. 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
47. 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
48. Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences H. Rafiq et al. 10.3390/plants13060833
49. Inversion of mine ventilation resistance coefficients enhanced by deep reinforcement learning P. Cao et al. 10.1016/j.psep.2023.12.005
50. 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
51. 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
52. Potential of globally distributed topsoil mid-infrared spectral library for organic carbon estimation Y. Hong et al. 10.1016/j.catena.2023.107628
53. 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
54. 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
55. 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
56. 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
57. High-resolution mapping of soil carbon stocks in the western Amazon C. Moquedace et al. 10.1016/j.geodrs.2024.e00773
58. Quantitative Analysis of Near-Infrared Spectroscopy of Blended Fabrics Based on Convolutional Neural Network Y. Tao 10.1109/ACCESS.2023.3275430
59. 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
60. 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
61. 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
62. High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
63. 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
64. Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library X. Zhang et al. 10.3390/rs15020465
65. Soil texture prediction with automated deep convolutional neural networks and population-based learning O. Omondiagbe et al. 10.1016/j.geoderma.2023.116521
66. A Study on Sample Size Sensitivity of Factory Manufacturing Dataset for CNN-Based Defective Product Classification D. Kim et al. 10.3390/computation10080142
67. 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
68. 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
69. 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
70. Deep learning for near-infrared spectral data modelling: Hypes and benefits P. Mishra et al. 10.1016/j.trac.2022.116804
71. Temperature interpretation method for temperature indicating paint based on spectrogram J. Ge et al. 10.1016/j.measurement.2023.113317
72. Estimating scattering potentials in inverse problems with Volterra series and neural networks G. Balassa 10.1140/epja/s10050-022-00839-y
73. 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
74. Sample Size Optimization for Digital Soil Mapping: An Empirical Example D. Saurette et al. 10.3390/land13030365
75. Deep Learning‐Assisted Design of Novel Promoters in Escherichia coli X. Wang et al. 10.1002/ggn2.202300184
76. 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
77. 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
78. Zero-day attack detection: a systematic literature review R. Ahmad et al. 10.1007/s10462-023-10437-z
79. Data fusion and network intrusion detection systems R. Ahmad & I. Alsmadi 10.1007/s10586-024-04365-y
80. Towards building data analytics benchmarks for IoT intrusion detection R. Ahmad et al. 10.1007/s10586-021-03388-z
81. Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher  et al. 10.3389/fenvs.2021.809995
82. 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
83. 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
84. 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
85. 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
86. Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation J. Lu et al. 10.3390/rs13173502