{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:34:01Z","timestamp":1781616841854,"version":"3.54.5"},"reference-count":87,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41701398"],"award-info":[{"award-number":["41701398"]}]},{"name":"National Natural Science Foundation of China","award":["42071240"],"award-info":[{"award-number":["42071240"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.<\/jats:p>","DOI":"10.3390\/rs16122190","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T06:29:43Z","timestamp":1718605783000},"page":"2190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhenghua","family":"Song","sequence":"first","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9797-4439","authenticated-orcid":false,"given":"Yanfu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5572-8839","authenticated-orcid":false,"given":"Junru","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5226-0441","authenticated-orcid":false,"given":"Yiming","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1692-470X","authenticated-orcid":false,"given":"Danyao","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6519-7128","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Water and Soil Conservation Science and Engineering, Northwest A&F University, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingrui","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Nature Resources and Environment, Northwest A&F University, Yangling District, Xianyang 712100, China"},{"name":"Key Laboratory of Plant Nutrition and Agri-Environment in Northwest China, Ministry of Agriculture, Yangling District, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"ref_1","first-page":"1953","article-title":"Advances in the Identification of Pathogens Associated with Apple Mosaic Disease of Apple Trees in China","volume":"37","author":"Xing","year":"2021","journal-title":"J. Fruit Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.agrformet.2008.03.005","article-title":"Estimating Chlorophyll Content from Hyperspectral Vegetation Indices: Modeling and Validation","volume":"148","author":"Wu","year":"2008","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/02827589509382904","article-title":"Chlorophyll Fluorescence: A Review of Its Practical Forestry Applications and Instrumentation","volume":"10","author":"Mohammed","year":"1995","journal-title":"Scand. J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Y., Jiang, D., Zhang, Z., and Chang, Q. (2023). Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content. Remote Sens., 15.","DOI":"10.3390\/rs15082202"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13007-020-00704-3","article-title":"Quantitative Visualization of Photosynthetic Pigments in Tea Leaves Based on Raman Spectroscopy and Calibration Model Transfer","volume":"17","author":"Zeng","year":"2021","journal-title":"Plant Methods"},{"key":"ref_6","first-page":"101038","article-title":"Approximation Techniques for Apple Disease Detection and Prediction Using Computer Enabled Technologies: A Review","volume":"32","author":"Sharma","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.isprsjprs.2023.05.021","article-title":"Dynamic Estimation of Rice Aboveground Biomass Based on Spectral and Spatial Information Extracted from Hyperspectral Remote Sensing Images at Different Combinations of Growth Stages","volume":"202","author":"Xu","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1016\/j.rse.2007.08.014","article-title":"Evaluation of Hyperspectral Data for Pasture Estimate in the Brazilian Amazon Using Field and Imaging Spectrometers","volume":"112","author":"Numata","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"135251","DOI":"10.1016\/j.foodchem.2022.135251","article-title":"A Deep Learning Method for Predicting Lead Content in Oilseed Rape Leaves Using Fluorescence Hyperspectral Imaging","volume":"409","author":"Zhou","year":"2023","journal-title":"Food Chem."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108929","DOI":"10.1016\/j.fcr.2023.108929","article-title":"Improving Chlorophyll Content Detection to Suit Maize Dynamic Growth Effects by Deep Features of Hyperspectral Data","volume":"297","author":"Zhao","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, J., Bai, T., and Li, X. (2024). Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network\u2013Random Forest\u2013Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels. Agronomy, 14.","DOI":"10.3390\/agronomy14010140"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107036","DOI":"10.1016\/j.compag.2022.107036","article-title":"Estimating the Distribution of Chlorophyll Content in CYVCV Infected Lemon Leaf Using Hyperspectral Imaging","volume":"198","author":"Li","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11119-023-10082-0","article-title":"Spectroscopic Determination of Chlorophyll Content in Sugarcane Leaves for Drought Stress Detection","volume":"25","author":"Gai","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TGRS.1995.8746029","article-title":"The Interpretation of Spectral Vegetation Indexes","volume":"33","author":"Myneni","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiao, J., Yan, K., Lu, X., Li, W., Tian, H., Wang, L., Deng, J., and Lan, Y. (2023). Advances and Developments in Monitoring and Inversion of the Biochemical Information of Crop Nutrients Based on Hyperspectral Technology. Agronomy, 13.","DOI":"10.3390\/agronomy13082163"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2008.06.002","article-title":"Deriving Leaf Chlorophyll Content of Green-Leafy Vegetables from Hyperspectral Reflectance","volume":"64","author":"Xue","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote Estimation of Canopy Chlorophyll Content in Crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS Terrestrial Chlorophyll Index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1017\/S0021859607007514","article-title":"Estimation of Leaf Total Chlorophyll and Nitrogen Concentrations Using Hyperspectral Satellite Imagery","volume":"146","author":"Rao","year":"2008","journal-title":"J. Agric. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108675","DOI":"10.1016\/j.compag.2024.108675","article-title":"Combined Use of Spectral Resampling and Machine Learning Algorithms to Estimate Soybean Leaf Chlorophyll","volume":"218","author":"Gao","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","first-page":"251","article-title":"Non-Destructive Estimation of Foliar Chlorophyll and Carotenoid Contents: Focus on Informative Spectral Bands","volume":"38","author":"Kira","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9802585","DOI":"10.34133\/2022\/9802585","article-title":"Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits","volume":"2022","author":"Shu","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pineda, M. (2021). An Overview of the Special Issue on Plant Phenotyping for Disease Detection. Remote Sens., 13.","DOI":"10.3390\/rs13204182"},{"key":"ref_24","first-page":"167","article-title":"Detection of Nitrogen Content in Lettuce Leaves Based on Spectroscopy and Texture Using Hyperspectral Imaging Technology","volume":"30","author":"Sun","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhang, K., Sun, Y., Zhao, Y., Zhuang, H., Ban, W., Chen, Y., Fu, E., Chen, S., and Liu, J. (2022). Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation. Remote Sens., 14.","DOI":"10.3390\/rs14020331"},{"key":"ref_26","first-page":"1603","article-title":"Study on the Early Detection of Early Blight on Tomato Leaves Using Hyperspectral Imaging Technique Based on Spectroscopy and Texture","volume":"33","author":"Xie","year":"2013","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_27","first-page":"568","article-title":"Detection of Chlorophyll Content of Epipremnum Aureum Based on Fusion of Spectrum and Texture Features","volume":"44","author":"Yan","year":"2021","journal-title":"J. Nanjing Agric. Univ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107358","DOI":"10.1016\/j.compag.2022.107358","article-title":"Estimation of Chlorophyll Distribution in Banana Canopy Based on RGB-NIR Image Correction for Uneven Illumination","volume":"202","author":"An","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1111\/j.1399-3054.2012.01639.x","article-title":"A New Optical Leaf-clip Meter for Simultaneous Non-destructive Assessment of Leaf Chlorophyll and Epidermal Flavonoids","volume":"146","author":"Cerovic","year":"2012","journal-title":"Physiol. Plant."},{"key":"ref_30","first-page":"724","article-title":"Recognition of Drought Stress in Tomato Based on Hyperspectral Imaging","volume":"43","author":"He","year":"2023","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9813841","DOI":"10.34133\/2022\/9813841","article-title":"Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves","volume":"2022","author":"Xiao","year":"2022","journal-title":"Plant Phenomics"},{"key":"ref_32","first-page":"923","article-title":"Fusion of Spectrum and Image Features to Identify Glycyrrhizae Radix et Rhizoma from Different Origins Based on Hyperspectral Imaging Technology","volume":"46","author":"Yin","year":"2021","journal-title":"Chin. Mater. Medica"},{"key":"ref_33","first-page":"1023","article-title":"Tobacco Disease Detection Model Based on Band Selection","volume":"43","author":"Pan","year":"2023","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The Successive Projections Algorithm for Variable Selection in Spectroscopic Multicomponent Analysis","volume":"57","author":"Saldanha","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_35","first-page":"100","article-title":"Classification and feature band extraction of diseased citrus plants based on UAV hyperspectral remote sensing","volume":"41","author":"Deng","year":"2020","journal-title":"J. South. China Agric. Univ."},{"key":"ref_36","first-page":"160","article-title":"Prediction of Winter Wheat Chlorophyll Content Based on Gram-Schmidt and SPXY Algorithm","volume":"48","author":"Mao","year":"2017","journal-title":"J. Agric. Mach."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.talanta.2005.03.025","article-title":"A Method for Calibration and Validation Subset Partitioning","volume":"67","author":"Galvao","year":"2005","journal-title":"Talanta"},{"key":"ref_38","first-page":"738","article-title":"The NIR Detection Research of Soluble Solid Content in Watermelon Based on SPXY Algorithm","volume":"39","author":"Wang","year":"2019","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_39","unstructured":"Kim, M.S., Daughtry, C., Chappelle, E., McMurtrey, J., and Walthall, C. (1994). The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynthetically Active Radiation (A Par), CNES, Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, Val d\u2019Isere, France, 17\u201324 January 1994."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/S0034-4257(98)00046-7","article-title":"Remote Sensing of Chlorophyll a, Chlorophyll b, Chlorophyll A+b, and Total Carotenoid Content in Eucalyptus Leaves","volume":"66","author":"Datt","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of Soil-Adjusted Vegetation Indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11119-008-9100-2","article-title":"Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves","volume":"10","author":"Devadas","year":"2009","journal-title":"Precis. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431160210163074","article-title":"Vegetation Indices Derived from High-Resolution Airborne Videography for Precision Crop Management","volume":"24","author":"Metternicht","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral Indices for Estimating Photosynthetic Pigment Concentrations: A Test Using Senescent Tree Leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0034-4257(02)00113-X","article-title":"Steady-State Chlorophyll a Fluorescence Detection from Canopy Derivative Reflectance and Double-Peak Red-Edge Effects","volume":"84","author":"Pushnik","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance Indices Associated with Physiological Changes in Nitrogen- and Water-Limited Sunflower Leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-destructive Optical Detection of Pigment Changes during Leaf Senescence and Fruit Ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiol. Plant."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1029\/2002GL016450","article-title":"Remote Estimation of Leaf Area Index and Green Leaf Biomass in Maize Canopies","volume":"30","author":"Gitelson","year":"2003","journal-title":"Geophys. Res. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating PAR Absorbed by Vegetation from Bidirectional Reflectance Measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/S0176-1617(96)80285-9","article-title":"Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 Nm","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_55","first-page":"327","article-title":"Non-Destructive and Remote Sensing Techniques for Estimation of Vegetation Status","volume":"3543","author":"Gitelson","year":"2001","journal-title":"Sch. Nat. Resour. Fac. Publ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/0034-4257(92)90089-3","article-title":"Ratio Analysis of Reflectance Spectra (RARS): An Algorithm for the Remote Estimation of the Concentrations of Chlorophyll A, Chlorophyll B, and Carotenoids in Soybean Leaves","volume":"39","author":"Chappelle","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2003.09.004","article-title":"Towards Universal Broad Leaf Chlorophyll Indices Using PROSPECT Simulated Database and Hyperspectral Reflectance Measurements","volume":"89","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/01431169308953986","article-title":"Red Edge Spectral Measurements from Sugar Maple Leaves","volume":"14","author":"Vogelmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","unstructured":"Barnes, E., Clarke, T.R., Richards, S.E., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T.L. (2000, January 16\u201319). Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground Based Multispectral Data. Proceedings of the International Conference on Precision Agriculture and Other Resource Management, Bloomington, MN, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"109175","DOI":"10.1016\/j.fcr.2023.109175","article-title":"Combining Vegetation, Color, and Texture Indices with Hyperspectral Parameters Using Machine-Learning Methods to Estimate Nitrogen Concentration in Rice Stems and Leaves","volume":"304","author":"Wang","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"108016","DOI":"10.1016\/j.compag.2023.108016","article-title":"Research on Rice Leaf Area Index Estimation Based on Fusion of Texture and Spectral Information","volume":"211","author":"Yuan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., and Cao, W. (2021). Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat. Remote Sens., 13.","DOI":"10.3390\/rs13183612"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s11119-016-9466-5","article-title":"Combining Spatial and Spectral Information to Estimate Chlorophyll Contents of Crop Leaves with a Field Imaging Spectroscopy System","volume":"18","author":"Liu","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Sudu, B., Rong, G., Guga, S., Li, K., Zhi, F., Guo, Y., Zhang, J., and Bao, Y. (2022). Retrieving SPAD Values of Summer Maize Using UAV Hyperspectral Data Based on Multiple Machine Learning Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14215407"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11119-018-9600-7","article-title":"Improved Estimation of Rice Aboveground Biomass Combining Textural and Spectral Analysis of UAV Imagery","volume":"20","author":"Zheng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Guo, W., Gong, Z., Gao, C., Yue, J., Fu, Y., Sun, H., Zhang, H., and Zhou, L. (2024). An Accurate Monitoring Method of Peanut Southern Blight Using Unmanned Aerial Vehicle Remote Sensing. Precis. Agric.","DOI":"10.1007\/s11119-024-10137-w"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"83628","DOI":"10.1007\/s11356-023-28344-9","article-title":"Improving Lake Chlorophyll-a Interpreting Accuracy by Combining Spectral and Texture Features of Remote Sensing","volume":"30","author":"Yang","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1126\/science.1205438","article-title":"Detecting Novel Associations in Large Data Sets","volume":"334","author":"Reshef","year":"2011","journal-title":"Science"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhu, W., Sun, Z., Peng, J., Huang, Y., Li, J., Zhang, J., Yang, B., and Liao, X. (2019). Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sens., 11.","DOI":"10.3390\/rs11222678"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"12247","DOI":"10.3390\/rs61212247","article-title":"Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods","volume":"6","author":"Doktor","year":"2014","journal-title":"Remote Sens."},{"key":"ref_72","first-page":"117","article-title":"Remote Sensing Inversion of Leaf Area Index Based on Support Vector Machine Regression in Winter Wheat","volume":"29","author":"Liang","year":"2013","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1007\/s11119-021-09804-z","article-title":"Hyperspectral Assessment of Leaf Nitrogen Accumulation for Winter Wheat Using Different Regression Modeling","volume":"22","author":"Guo","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-Propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ta, N., Chang, Q., and Zhang, Y. (2021). Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods. Remote Sens., 13.","DOI":"10.3390\/rs13193902"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geoderma.2006.07.004","article-title":"Determining the Composition of Mineral-Organic Mixes Using UV\u2013Vis\u2013NIR Diffuse Reflectance Spectroscopy","volume":"137","author":"McGlynn","year":"2006","journal-title":"Geoderma"},{"key":"ref_77","first-page":"74","article-title":"Study on Hyperspectral Remote Sensing in Estimate Vegetation Leaf Chlorophyll Content","volume":"21","author":"Zhang","year":"2003","journal-title":"J. Shanghai Jiaotong Univ."},{"key":"ref_78","first-page":"889","article-title":"Effect of Cucumber Mosaic Virus-Encoded 2b Protein on Photosynthesis and Chloroplast Structure of the Host Plant","volume":"34","author":"Chen","year":"2007","journal-title":"Prog. Biochem. Biophys."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.cell.2020.07.020","article-title":"A Defense Pathway Linking Plasma Membrane and Chloroplasts and Co-Opted by Pathogens","volume":"182","author":"Tan","year":"2020","journal-title":"Cell"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"155540","DOI":"10.1109\/ACCESS.2019.2949866","article-title":"Estimating Total Leaf Chlorophyll Content of Gannan Navel Orange Leaves Using Hyperspectral Data Based on Partial Least Squares Regression","volume":"7","author":"Peng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Sonobe, R., and Wang, Q. (2017). Towards a Universal Hyperspectral Index to Assess Chlorophyll Content in Deciduous Forests. Remote Sens., 9.","DOI":"10.3390\/rs9030191"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"105590","DOI":"10.1016\/j.ecolind.2019.105590","article-title":"Estimating Leaf Nitrogen Concentration Considering Unsynchronized Maize Growth Stages with Canopy Hyperspectral Technique","volume":"107","author":"Wen","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"108559","DOI":"10.1016\/j.compag.2023.108559","article-title":"Comparison of Leaf Chlorophyll Content Retrieval Performance of Citrus Using FOD and CWT Methods with Field-Based Full-Spectrum Hyperspectral Reflectance Data","volume":"217","author":"Xiao","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Shah, S.H., Angel, Y., Houborg, R., Ali, S., and McCabe, M.F. (2019). A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat. Remote Sens., 11.","DOI":"10.3390\/rs11080920"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"112940","DOI":"10.1016\/j.scienta.2024.112940","article-title":"Improved Estimation of SPAD Values in Walnut Leaves by Combining Spectral, Texture, and Structural Information from UAV-Based Multispectral Image","volume":"328","author":"Wang","year":"2024","journal-title":"Sci. Hortic."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"106000","DOI":"10.1016\/j.compag.2021.106000","article-title":"Which Multispectral Indices Robustly Measure Canopy Nitrogen across Seasons: Lessons from an Irrigated Pasture Crop","volume":"182","author":"Patel","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., and Ruan, C. (2020). Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12091419"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:59:44Z","timestamp":1760108384000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,17]]},"references-count":87,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16122190"],"URL":"https:\/\/doi.org\/10.3390\/rs16122190","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,17]]}}}