Vis-NIR spectra combined with machine learning for predicting soil nutrients in cropland from Aceh Province, Indonesia
Tài liệu tham khảo
Marques, 2016, Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer, Food Chem., 197, 1207, 10.1016/j.foodchem.2015.11.080
Pudełko, 2020, Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods, Geoderma, 368, 10.1016/j.geoderma.2020.114306
Yubing, 2021, Predicting organic matter content, total nitrogen and pH value of lime concretion black soil based on visible and near infrared spectroscopy, Eurasian Soil Sci., 54, 1681, 10.1134/S1064229321110144
Lei, 2022, Achieving joint calibration of soil Vis-NIR spectra across instruments, soil types and properties by an attention-based spectra encoding-spectra/property decoding architecture, Geoderma, 405, 10.1016/j.geoderma.2021.115449
Clingensmith, 2022, Predicting soil properties and interpreting vis-NIR models from across continental United States, Sensors, 22, 3187, 10.3390/s22093187
Gruszczyński, 2022, Supporting soil and land assessment with machine learning models using the Vis-NIR spectral response, Geoderma, 405, 10.1016/j.geoderma.2021.115451
Soriano-Disla, 2014, The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties, Appl. Spectrosc. Rev., 49, 139, 10.1080/05704928.2013.811081
Stenberg, 2010, Chapter five - visible and near infrared spectroscopy in soil science, vol. 107, 163
Cécillon, 2009, Assessment and monitoring of soil quality using near-infrared reflectance spectroscopy (NIRS), Eur. J. Soil Sci., 60, 770, 10.1111/j.1365-2389.2009.01178.x
Daniel, 2003, Artificial neural network analysis of laboratory and in situ spectra for the estimation of macronutrients in soils of Lop Buri (Thailand), Aust. J. Soil Res., 41, 47, 10.1071/SR02027
Fidêncio, 2002, Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy, Anal. Chim. Acta, 453, 125, 10.1016/S0003-2670(01)01506-9
Conforti, 2018, Using laboratory Vis-NIR spectroscopy for monitoring some forest soil properties, J. Soils Sediments, 18, 1009, 10.1007/s11368-017-1766-5
Zhao, 2021, Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: effects of multiple calibration models and spiking, Comput. Electron. Agric., 182, 10.1016/j.compag.2021.105990
Zhao, 2021, Predicting soil physical and chemical properties using vis-NIR in Australian cotton areas, Catena, 196, 10.1016/j.catena.2020.104938
Yang, 2019, Evaluation of machine learning approaches to predict soil organic matter and pH using Vis-NIR spectra, Sensors, 19, 263, 10.3390/s19020263
Morellos, 2016, Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy, Biosyst. Eng., 152, 104, 10.1016/j.biosystemseng.2016.04.018
Nawar, 2019, On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning, Soil Tillage Res., 190, 120, 10.1016/j.still.2019.03.006
Kim, 2012, SVM tutorial-classification, regression and ranking, Handbook of Natural computing, 1, 479
Al-Mashhadani, 2020, Survey of agricultural robot applications and implementation, 10.1109/IEMCON51383.2020.9284910
Pathan, 2019, Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning, Sci. Rep., 9, 10.1038/s41598-019-50144-w
Friedman, 2001, Greedy function approximation: a gradient boosting machine, Ann. Stat., 1189
Cao, 2022, Calibration of near-infrared spectra for phosphorus fractions in grassland soils on the Tibetan plateau, Agronomy, 12, 10.3390/agronomy12040783
Rodríguez-Febereiro, 2022, Evaluation of spectroscopy and methodological pre-treatments to estimate soil nutrients in the vineyard, Rem. Sens., 14, 1326, 10.3390/rs14061326
Peng, 2021, Estimation of soil nutrient content using hyperspectral data, Agriculture, 11, 1129, 10.3390/agriculture11111129
Guo, 2021, Evaluating calibration and spectral variable selection methods for predicting three soil nutrients using vis-NIR spectroscopy, Rem. Sens., 13, 4000, 10.3390/rs13194000
Liu, 2021, Estimation of soil organic matter content based on CARS algorithm coupled with random forest, Spectrochim. Acta Mol. Biomol. Spectrosc., 258, 10.1016/j.saa.2021.119823
Iso, 1995
Campbell, 2019, Digital soil mapping of soil properties in the “Mar de Morros” environment using spectral data, Rev. Bras. Ciência do Solo, 42
Eskildsen, 2019, Visualizing indirect correlations when predicting fatty acid composition from near infrared spectroscopy measurements
Huang, 2020, Measurement of early disease blueberries based on vis/NIR hyperspectral imaging system, Sensors, 20, 5783, 10.3390/s20205783
Jiang, 2016, Estimating soil organic carbon of cropland soil at different levels of soil moisture using VIS-NIR spectroscopy, Rem. Sens., 8, 755, 10.3390/rs8090755
Kunze, 2021, Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA, Microchem. J., 171, 10.1016/j.microc.2021.106839
Munawar, 2022, Near infrared spectroscopy as a fast and non-destructive technique for total acidity prediction of intact mango: comparison among regression approaches, Comput. Electron. Agric., 193, 10.1016/j.compag.2021.106657
Nicolaï, 2007, Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review, Postharvest Biol. Technol., 46, 99, 10.1016/j.postharvbio.2007.06.024
Walsh, 2006, Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content, Anal. Chim. Acta, 555, 286, 10.1016/j.aca.2005.09.014
Chauchard, 2004, Correction of the temperature effect on near infrared calibration-application to soluble solid content prediction, J. Near Infrared Spectrosc., 12, 199, 10.1255/jnirs.427
Roger, 2003, EPO-PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits, Chemometr. Intell. Lab. Syst., 66, 191, 10.1016/S0169-7439(03)00051-0
Munnaf, 2021, A combined data mining approach for on-line prediction of key soil quality indicators by Vis-NIR spectroscopy, Soil Tillage Res., 205, 10.1016/j.still.2020.104808
Fystro, 2002, The prediction of C and N content and their potential mineralisation in heterogeneous soil samples using Vis-NIR spectroscopy and comparative methods, Plant Soil, 246, 139, 10.1023/A:1020612319014
Lal, 2012, Detection of Mg spinel lithologies on central peak of crater Theophilus using Moon Mineralogy Mapper (M3) data from Chandrayaan-1, J. Earth Syst. Sci., 121, 847, 10.1007/s12040-012-0193-7
Franceschini, 2018, Effects of external factors on soil reflectance measured on-the-go and assessment of potential spectral correction through orthogonalisation and standardisation procedures, Soil Tillage Res., 177, 19, 10.1016/j.still.2017.10.004
de Santana, 2021, Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries, Geoderma Regional, 27, 10.1016/j.geodrs.2021.e00436
Mouazen, 2010, Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy, Geoderma, 158, 23, 10.1016/j.geoderma.2010.03.001
Demattê, 2017, Chemometric soil analysis on the determination of specific bands for the detection of magnesium and potassium by spectroscopy, Geoderma, 288, 8, 10.1016/j.geoderma.2016.11.013
Guerrero, 2016, Do we really need large spectral libraries for local scale SOC assessment with NIR spectroscopy?, Soil Tillage Res., 155, 501, 10.1016/j.still.2015.07.008
Minasny, 2011, Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon, Geoderma, 167–168, 118, 10.1016/j.geoderma.2011.09.008
Zhang, 2016, Soil nitrogen content forecasting based on real-time NIR spectroscopy, Comput. Electron. Agric., 124, 29, 10.1016/j.compag.2016.03.016
de Santana, 2019, Removing the moisture effect in soil organic matter determination using NIR spectroscopy and PLSR with external parameter orthogonalization, Microchem. J., 145, 1094, 10.1016/j.microc.2018.12.027
Chebrolu, 2017, Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields, Int. J. Robot Res., 36, 1045, 10.1177/0278364917720510
Fernández, 2015, VIS-NIR, SWIR and LWIR imagery for estimation of ground bearing capacity, Sensors, 15, 13994, 10.3390/s150613994
