乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:15:16Z","timestamp":1740154516632,"version":"3.37.3"},"reference-count":74,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["1827551"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate, precise, and timely estimation of crop yield is key to a grower\u2019s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400\u20131000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78\u20130.93) and low root-mean-square error (RMSE = 940\u20131369 kg\/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83\u20130.98) and lower errors (RMSE = 4.245\u20136.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44\u201355 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward.<\/jats:p>","DOI":"10.3390\/rs13163241","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T02:51:27Z","timestamp":1629082287000},"page":"3241","source":"Crossref","is-referenced-by-count":17,"title":["Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0171-238X","authenticated-orcid":false,"given":"Amirhossein","family":"Hassanzadeh","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6329-9527","authenticated-orcid":false,"given":"Fei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3036-0088","authenticated-orcid":false,"given":"Jan","family":"van Aardt","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8500-7524","authenticated-orcid":false,"given":"Sean P.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech at The New York State Agricultural Experiment Station, Cornell University, Geneva, NY 14456, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3864-4293","authenticated-orcid":false,"given":"Sarah J.","family":"Pethybridge","sequence":"additional","affiliation":[{"name":"Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell AgriTech at The New York State Agricultural Experiment Station, Cornell University, Geneva, NY 14456, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,15]]},"reference":[{"key":"ref_1","unstructured":"(2021, January 24). Ag and Food Sectors and the Economy, Available online: https:\/\/www.ers.usda.gov\/data-products\/ag-and-food-statistics-charting-the-essentials\/ag-and-food-sectors-and-the-economy."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.eja.2008.05.005","article-title":"Precision agriculture on grassland: Applications, perspectives and constraints","volume":"29","author":"Schellberg","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"024519","DOI":"10.1117\/1.JRS.14.024519","article-title":"Yield modeling of snap bean based on hyperspectral sensing: A greenhouse study","volume":"14","author":"Hassanzadeh","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hassanzadeh, A., Murphy, S.P., Pethybridge, S.J., and van Aardt, J. (2020). Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study. Remote Sens., 12.","DOI":"10.3390\/rs12223809"},{"key":"ref_5","first-page":"32","article-title":"Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture","volume":"43","author":"Elarab","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.chemolab.2017.12.010","article-title":"Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf","volume":"172","author":"Yu","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.fcr.2007.03.023","article-title":"Remote sensing of nitrogen and water stress in wheat","volume":"104","author":"Tilling","year":"2007","journal-title":"Field Crop. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11119-004-5321-1","article-title":"A review on remote sensing of weeds in agriculture","volume":"5","author":"Thorp","year":"2004","journal-title":"Precis. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compag.2007.02.010","article-title":"Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy","volume":"61","author":"Christy","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jfoodeng.2009.01.028","article-title":"Multispectral images of peach related to firmness and maturity at harvest","volume":"93","author":"Barreiro","year":"2009","journal-title":"J. Food Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.compag.2010.08.005","article-title":"Sensing technologies for precision specialty crop production","volume":"74","author":"Lee","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107886","DOI":"10.1016\/j.agrformet.2019.107886","article-title":"Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil","volume":"284","author":"Schwalbert","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.isprsjprs.2020.02.013","article-title":"Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging","volume":"162","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MSP.2013.2278992","article-title":"Hyperspectral target detection: An overview of current and future challenges","volume":"31","author":"Nasrabadi","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1943","DOI":"10.1111\/j.1749-8198.2008.00182.x","article-title":"Hyperspectral remote sensing of vegetation","volume":"2","author":"Im","year":"2008","journal-title":"Geogr. Compass"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3032","DOI":"10.1016\/j.patcog.2009.04.008","article-title":"On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images","volume":"42","author":"Plaza","year":"2009","journal-title":"Pattern Recognition"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rasti, B., Scheunders, P., Ghamisi, P., Licciardi, G., and Chanussot, J. (2018). Noise reduction in hyperspectral imagery: Overview and application. Remote Sens., 10.","DOI":"10.3390\/rs10030482"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1109\/TGRS.2012.2185054","article-title":"Hyperspectral image denoising employing a spectral\u2013spatial adaptive total variation model","volume":"50","author":"Yuan","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"117","article-title":"Hyperspectral imaging system modeling","volume":"14","author":"Kerekes","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_23","first-page":"29","article-title":"Compensation of hyperspectral data for atmospheric effects","volume":"14","author":"Griffin","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_24","unstructured":"Antczak, K. (2018). Deep recurrent neural networks for ECG signal denoising. arXiv, Available online: https:\/\/arxiv.org\/abs\/1807.11551v3."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1109\/LSP.2019.2939049","article-title":"Deep learning denoising based line spectral estimation","volume":"26","author":"Jiang","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104063","DOI":"10.1016\/j.chemolab.2020.104063","article-title":"Noise reduction in the spectral domain of hyperspectral images using denoising autoencoder methods","volume":"203","author":"Zhang","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1366\/0003702053945985","article-title":"Investigation of selected baseline removal techniques as candidates for automated implementation","volume":"59","author":"Schulze","year":"2005","journal-title":"Appl. Spectrosc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.petrol.2008.12.002","article-title":"Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization","volume":"66","author":"Chen","year":"2009","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.foodres.2008.11.008","article-title":"Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening, winemaking, and aging of white and red wines","volume":"42","author":"Morales","year":"2009","journal-title":"Food Res. Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/97.475823","article-title":"Noise reduction using an undecimated discrete wavelet transform","volume":"3","author":"Lang","year":"1996","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_31","unstructured":"Jansen, M. (2012). Noise Reduction by Wavelet Thresholding, Springer Science & Business Media."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/TGRS.2010.2075937","article-title":"Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage","volume":"49","author":"Chen","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1037\/0033-2909.99.3.432","article-title":"Comparison of five rules for determining the number of components to retain","volume":"99","author":"Zwick","year":"1986","journal-title":"Psychol. Bull."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1111\/j.2044-8317.1950.tb00285.x","article-title":"Tests of significance in factor analysis","volume":"3","author":"Bartlett","year":"1950","journal-title":"Br. J. Stat. Psychol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/001316446002000116","article-title":"The application of electronic computers to factor analysis","volume":"20","author":"Kaiser","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1177\/001316447603600114","article-title":"The relation between factor score estimates, image scores, and principal component scores","volume":"36","author":"Velicer","year":"1976","journal-title":"Educ. Psychol. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1207\/s15327906mbr0102_10","article-title":"The scree test for the number of factors","volume":"1","author":"Cattell","year":"1966","journal-title":"Multivar. Behav. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/BF02289447","article-title":"A rationale and test for the number of factors in factor analysis","volume":"30","author":"Horn","year":"1965","journal-title":"Psychometrika"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded recurrent neural networks for hyperspectral image classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Agarwal, A., El-Ghazawi, T., El-Askary, H., and Le-Moigne, J. (2007, January 15\u201318). Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. Proceedings of the 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza, Egypt.","DOI":"10.1109\/ISSPIT.2007.4458191"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Benesty, J., Chen, J., Huang, Y., and Cohen, I. (2009). Pearson correlation coefficient. Noise Reduction in Speech Processing, Springer.","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1021\/ci0342472","article-title":"The problem of overfitting","volume":"44","author":"Hawkins","year":"2004","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_43","first-page":"3","article-title":"A review of feature selection and its methods","volume":"19","author":"Venkatesh","year":"2019","journal-title":"Cybern. Inf. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"London Edinburgh Dublin Philos. Mag. J. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/72.298224","article-title":"Using mutual information for selecting features in supervised neural net learning","volume":"5","author":"Battiti","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"211","DOI":"10.6029\/smartcr.2014.03.007","article-title":"Feature selection: A literature review","volume":"4","author":"Kumar","year":"2014","journal-title":"SmartCR"},{"key":"ref_47","unstructured":"Goldenberg, D.E. (2021, January 24). Genetic Algorithms in Search, Optimization and Machine Learning. Available online: https:\/\/deepblue.lib.umich.edu\/bitstream\/handle\/2027.42\/46947\/10994_2005_Article_422926.pdf."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, B., Wang, L., and Song, W. (2008, January 18\u201320). Ant colony optimization for the traveling salesman problem based on ants with memory. Proceedings of the 2008 Fourth International Conference on Natural Computation, Jinan, China.","DOI":"10.1109\/ICNC.2008.354"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TEVC.2007.896686","article-title":"Particle swarm optimization: Basic concepts, variants and applications in power systems","volume":"12","author":"Venayagamoorthy","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"John, G.H., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. Machine Learning Proceedings 1994, Elsevier.","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1093\/bib\/bbn027","article-title":"Penalized feature selection and classification in bioinformatics","volume":"9","author":"Ma","year":"2008","journal-title":"Briefings Bioinform."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X., and Fan, L. (2020). Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors, 20.","DOI":"10.3390\/s20041231"},{"key":"ref_53","unstructured":"USDA (2021, January 24). USDA National Agricultural Statistics Service; National Statistical Services, Available online: https:\/\/quickstats.nass.usda.gov\/results\/5B4DC16F-4537-38BB-86AC-010B3FBF83DC?pivot=short_desc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.fcr.2017.06.014","article-title":"A simple model for snap bean (Phaseolus vulgaris L.) development, growth and yield in response to nitrogen","volume":"211","author":"Yuan","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Saleh, S., Liu, G., Liu, M., Ji, Y., He, H., and Gruda, N. (2018). Effect of Irrigation on Growth, Yield, and Chemical Composition of Two Green Bean Cultivars. Horticulturae, 4.","DOI":"10.3390\/horticulturae4010003"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kaputa, D.S., Bauch, T., Roberts, C., McKeown, D., Foote, M., and Salvaggio, C. (2019, January 25\u201327). Mx-1: A new multi-modal remote sensing uas payload with high accuracy gps and imu. Proceedings of the 2019 IEEE Systems and Technologies for Remote Sensing Applications Through Unmanned Aerial Systems (STRATUS), Rochester, NY, USA.","DOI":"10.1109\/STRATUS.2019.8713292"},{"key":"ref_57","unstructured":"Mamaghani, B., and Salvaggio, C. (2019). Comparative study of panel and panelless-based reflectance conversion techniques for agricultural remote sensing. arXiv, Available online: https:\/\/arxiv.org\/abs\/1910.03734."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/0734-189X(90)90053-X","article-title":"A comparative performance study of several global thresholding techniques for segmentation","volume":"52","author":"Lee","year":"1990","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_59","unstructured":"Yuhas, R.H., Goetz, A.F., and Boardman, J.W. (1992, January 1\u20135). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. Proceedings of the Summaries 3rd Annual JPL Airborne Geoscience Workshop, Pasadena, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1177\/0013164410389489","article-title":"Performance of Velicer\u2019s minimum average partial factor retention method with categorical variables","volume":"71","author":"Garrido","year":"2011","journal-title":"Educ. Psychol. Meas."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Price, K.V. (2013). Differential evolution. Handbook of Optimization, Springer.","DOI":"10.1007\/978-3-642-30504-7_8"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Van Laarhoven, P.J., and Aarts, E.H. (1987). Simulated annealing. Simulated Annealing: Theory and Applications, Springer.","DOI":"10.1007\/978-94-015-7744-1"},{"key":"ref_63","first-page":"115","article-title":"Universal sequential search problems","volume":"9","author":"Levin","year":"1973","journal-title":"Probl. Peredachi Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1093\/bioinformatics\/btq134","article-title":"Permutation importance: A corrected feature importance measure","volume":"26","author":"Altmann","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_66","unstructured":"Van Rossum, G., and Drake, F.L. (1995). Python Tutorial, Centrum voor Wiskunde en Informatica. Available online: http:\/\/lib.21h.io\/library\/G7B3RFY7\/download\/GBMFTU3C\/Python_3.8.3_Docs_.pdf."},{"key":"ref_67","unstructured":"Bergstra, J., Yamins, D., and Cox, D.D. (2013, January 12\u201318). Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. Proceedings of the 12th Python in Science Conference, Citeseer, Austin, TX, USA. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.704.3494&rep=rep1&type=pdf."},{"key":"ref_68","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Bajorski, P. (2011). Statistics for Imaging, Optics, and Photonics, John Wiley & Sons.","DOI":"10.1002\/9781118121955"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S1872-2032(08)60018-9","article-title":"Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing","volume":"28","author":"Wei","year":"2008","journal-title":"Acta Ecol. Sin."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.eja.2006.12.001","article-title":"Canopy reflectance in cotton for growth assessment and lint yield prediction","volume":"26","author":"Zhao","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.plantsci.2018.10.022","article-title":"A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform","volume":"282","author":"Hassan","year":"2019","journal-title":"Plant Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3241\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T05:04:18Z","timestamp":1721192658000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,15]]},"references-count":74,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163241"],"URL":"https:\/\/doi.org\/10.3390\/rs13163241","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,8,15]]}}}