125 citations as recorded by crossref.

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9. Label‐Free Blood Typing by Raman Spectroscopy and Artificial Intelligence E. Jensen et al. 10.1002/admt.202301462
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21. Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning M. Widyastuti et al. 10.5194/soil-11-287-2025
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29. Orchard classification based on super-pixels and deep learning with sparse optical images J. Li et al. 10.1016/j.compag.2023.108379
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31. Rapid detection and visualization of physiological signatures in cotton leaves under Verticillium wilt stress N. Wu et al. 10.1016/j.aiia.2025.06.002
32. 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
33. Soil Organic Carbon Prediction and Mapping in Morocco Using PRISMA Hyperspectral Imagery and Meta-Learner Model Y. Bouslihim et al. 10.3390/rs17081363
34. A Convolutional Neural Network for Steady-State Flow Approximation Trained on a Small Sample Size G. Zhong et al. 10.3390/atmos14091462
35. 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
36. 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
37. Improving Soil Organic Matter Mapping Using Transfer Learning and Satellite-Simulated Samples From Bare Soil Hyperspectral Imagery X. Xu et al. 10.1109/JSTARS.2024.3508817
38. 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
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41. 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
42. 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
43. 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
44. 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
45. High-resolution mapping of soil carbon stocks in the western Amazon C. Moquedace et al. 10.1016/j.geodrs.2024.e00773
46. Quantitative Analysis of Near-Infrared Spectroscopy of Blended Fabrics Based on Convolutional Neural Network Y. Tao 10.1109/ACCESS.2023.3275430
47. Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library D. Zhang et al. 10.3390/acoustics6030037
48. 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
49. 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
50. 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
51. High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
52. A CNN-based self-supervised learning framework for small-sample near-infrared spectroscopy classification R. Zhao et al. 10.1039/D4AY01970A
53. A Study on Sample Size Sensitivity of Factory Manufacturing Dataset for CNN-Based Defective Product Classification D. Kim et al. 10.3390/computation10080142
54. 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
55. Assessing data and sample complexity in unmanned aerial vehicle imagery for agricultural pattern classification L. Arslanova et al. 10.1016/j.atech.2025.100799
56. Advancements in Soft-Sensor Technologies for Quality Control in Process Manufacturing: A Review J. Gallareta et al. 10.1109/JSEN.2025.3549596
57. Fresh Leaf Spectroscopy to Estimate the Crop Nutrient Status of Potato (Solanum tuberosum L.) A. Sharma et al. 10.1007/s11540-024-09766-5
58. 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
59. Estimating scattering potentials in inverse problems with Volterra series and neural networks G. Balassa 10.1140/epja/s10050-022-00839-y
60. 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
61. Sample Size Optimization for Digital Soil Mapping: An Empirical Example D. Saurette et al. 10.3390/land13030365
62. Predicting soil properties for fertiliser recommendation in South Korea using MIR spectroscopy S. Jeon et al. 10.1016/j.geodrs.2024.e00901
63. Infrared spectroscopy for soil NPK estimation: Advances, challenges, and future directions in predictive modelling S. Huai et al. 10.1016/j.trac.2025.118142
64. 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
65. 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
66. 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
67. Estimation of SOC using VNIR and MIR hyperspectral data based on spectral-to-image transforming and multi-channel CNN A. Tang et al. 10.1016/j.compag.2025.109986
68. A fair evaluation of the potential of machine learning in maritime transportation X. Luo et al. 10.3934/era.2023243
69. Metabolic Fluxes Using Deep Learning Based on Enzyme Variations: Application to Glycolysis in Entamoeba histolytica F. Oulia et al. 10.3390/ijms252413390
70. 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
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72. Developing a near-infrared spectroscopy calibration algorithm for soil organic carbon content in South Africa W. Cloete et al. 10.1016/j.soilad.2025.100039
73. Geospatial prediction of total soil carbon in European agricultural land based on deep learning D. Radočaj et al. 10.1016/j.scitotenv.2023.169647
74. 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
75. 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
76. 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
77. Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites H. Fu et al. 10.1016/j.ymeth.2024.12.010
78. Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign D. Jiang et al. 10.3390/s24041245
79. Uncertainty quantification for structural response field with ultra-high dimensions L. Cao & Y. Zhao 10.1016/j.ijmecsci.2024.109110
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81. Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction Y. Hong et al. 10.1016/j.rse.2025.114874
82. Prediction of Soil Erodibility by Diffuse Reflectance Spectroscopy in a Neotropical Dry Forest Biome S. Pontes et al. 10.3390/land11122188
83. Impact of calibration set size for predicting soil fertility attributes using local pXRF spectral libraries J. Ribeiro et al. 10.1016/j.soilad.2024.100031
84. 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
85. 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
86. Real-time detection of ammonium in soil pore water R. Yupiter et al. 10.1038/s41545-023-00243-z
87. Improving the Performance of Automated Rooftop Extraction through Geospatial Stratified and Optimized Sampling Z. Sun et al. 10.3390/rs14194961
88. Data mining of urban soil spectral library for estimating organic carbon Y. Hong et al. 10.1016/j.geoderma.2022.116102
89. 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
90. A CNN model for predicting soil properties using VIS–NIR spectral data M. Hosseinpour-Zarnaq et al. 10.1007/s12665-023-11073-0
91. Soil Surface Texture Classification Using RGB Images Acquired Under Uncontrolled Field Conditions E. Babalola et al. 10.1109/ACCESS.2023.3290907
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101. 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
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108. 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
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123. 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
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