SOIL TEXTURE CLASSIFICATION WITH 1D CONVOLUTIONAL NEURAL NETWORKS BASED ON HYPERSPECTRAL DATA

34 citations as recorded by crossref.

1. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies P. Maier et al. https://doi.org/10.3390/rs13040718
2. STAR: Soil texture analysis recognizer integrating domain-adaptive transfer learning with NIR spectroscopy Y. Luo et al. https://doi.org/10.1016/j.jenvman.2025.128378
3. Deep Learning for Chemometric Analysis of Plastic Spectral Data from Infrared and Raman Databases E. Neo et al. https://doi.org/10.2139/ssrn.4134033
4. Impact of Exposure and Illumination on Texture Classification Based on Raw Spectral Filter Array Images O. Elezabi et al. https://doi.org/10.3390/s23125443
5. Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning J. Justo et al. https://doi.org/10.1109/JSTARS.2024.3487360
6. Construction of complex features for predicting soil total nitrogen content based on convolution operations Y. Wang et al. https://doi.org/10.1016/j.still.2021.105109
7. AI and machine learning for soil analysis: an assessment of sustainable agricultural practices M. Awais et al. https://doi.org/10.1186/s40643-023-00710-y
8. Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area J. Florath & S. Keller https://doi.org/10.3390/rs14030657
9. Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users I. Cacciari & A. Ranfagni https://doi.org/10.3390/app14188500
10. Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery S. Hong et al. https://doi.org/10.1080/15481603.2022.2037887
11. Spectroscopic measurement of near-infrared soil pH parameters based on GhostNet-CBAM J. Zhu et al. https://doi.org/10.1371/journal.pone.0325426
12. Deep Learning for Land Cover Change Detection O. Sefrin et al. https://doi.org/10.3390/rs13010078
13. Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network N. Mohd Yusof et al. https://doi.org/10.1007/s11030-021-10289-1
14. Soil classification with multi-temporal hyperspectral imagery using spectral unmixing and fusion E. Kaba & U. Leloglu https://doi.org/10.1117/1.JRS.17.044513
15. Predicting USCS soil texture classes utilizing soil spectra and deep learning A. Kasaragod et al. https://doi.org/10.1007/s11368-024-03921-8
16. Advancements in Soil Classification: An In-Depth Analysis of Current Deep Learning Techniques and Emerging Trends E. Gyasi & S. Purushotham https://doi.org/10.1177/11786221231214069
17. Physics-guided neural network for predicting chemical signatures C. Murphy & J. Kerekes https://doi.org/10.1364/AO.420688
18. Soil-MobiNet: A Convolutional Neural Network Model Base Soil Classification to Determine Soil Morphology and Its Geospatial Location E. Gyasi & S. Purushotham https://doi.org/10.3390/s23156709
19. Enhancing Rock Strength Prediction and Features Selection by Coupling Well Log Data and Deep Learning Approaches in Reservoir Geomechanics M. Miah et al. https://doi.org/10.1002/nag.70356
20. Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India T. Tarun Kshatriya et al. https://doi.org/10.3390/agronomy14112707
21. A deep residual convolutional neural network for mineral classification N. Agrawal & H. Govil https://doi.org/10.1016/j.asr.2022.12.028
22. DSCformer: Lightweight model for predicting soil nitrogen content using VNIR-SWIR spectroscopy C. Li et al. https://doi.org/10.1016/j.compag.2024.109761
23. Data-driven resilient model development and feature selection for rock compressive strength prediction using machine learning and transformer techniques M. Rahaman & M. Miah https://doi.org/10.1007/s12145-025-01803-2
24. On-site soil analysis: A novel approach combining NIR spectroscopy, remote sensing and deep learning M. Kok et al. https://doi.org/10.1016/j.geoderma.2024.116903
25. A Review on Innovations in Soil Remediation Techniques Using Machine Learning M. Hajari et al. https://doi.org/10.1051/e3sconf/202455201134
26. Ex situ hyperspectral sensing and machine learning for tailings characterization J. Bindner et al. https://doi.org/10.1139/cgj-2024-0656
27. Soil data augmentation and model construction based on spectral difference and content difference L. Wan et al. https://doi.org/10.1016/j.saa.2024.124360
28. Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy Y. Wang et al. https://doi.org/10.3390/s20247078
29. Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks A. Santos et al. https://doi.org/10.3390/s20205762
30. Applications of Convolutional Neural Network for Classification of Land Cover and Groundwater Potentiality Zones A. Tegegne & G. Angiulli https://doi.org/10.1155/2022/6372089
31. Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model A. Soni et al. https://doi.org/10.1016/j.foodres.2021.110577
32. Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks L. Zhong et al. https://doi.org/10.1016/j.geoderma.2021.115366
33. Multifunctional Electrodes for Signal Soil Measurements: Benchmark With ML-Based Algorithms A. Sabatini et al. https://doi.org/10.1109/MPRV.2025.3574965
34. 1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures C. Murphy & J. Kerekes https://doi.org/10.1255/jsi.2021.a2