{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T07:35:53Z","timestamp":1776670553832,"version":"3.51.2"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Verilytix Inc.","award":["CON000000095565"],"award-info":[{"award-number":["CON000000095565"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral imaging is an emerging non-invasive technology with potential for early nutrient stress detection in plants prior to visible symptoms. This study evaluated hyperspectral imaging for early identification of nitrogen, phosphorus, and potassium (NPK) deficiencies across three greenhouse-grown industrial hemp plant cultivars (Cannabis sativa L.). Visible and near-infrared spectral data (380\u20131022 nm) were acquired from hemp samples subjected to controlled NPK stresses at multiple developmental timepoints using a benchtop hyperspectral camera. Robust principal component analysis was developed for effective screening of spectral outliers. Partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) were developed and optimized to classify nutrient deficiencies using key wavelengths selected by variable importance in projection (VIP) and interval partial least squares (iPLS). The 16-wavelength iPLS-C-SVM model achieved the highest precision of 0.75 to 1 on the test dataset. Key wavelengths for effective nutrient deficiency detection spanned the visible range, underscoring the hyperspectral imaging sensitivity to early changes in leaf pigment levels prior to any visible symptom development. The emergence of wavelengths related to chlorophyll, carotenoid, and anthocyanin absorption as optimal for classification, highlights the technology\u2019s capacity to detect subtle impending biochemical perturbations linked to emerging deficiencies. Identifying stress at this pre-visual stage could provide hemp producers with timely corrective action to mitigate losses in crop quality and yields.<\/jats:p>","DOI":"10.3390\/rs16010187","type":"journal-article","created":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T10:36:59Z","timestamp":1704191819000},"page":"187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Noninvasive Early Detection of Nutrient Deficiencies in Greenhouse-Grown Industrial Hemp Using Hyperspectral Imaging"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4582-7963","authenticated-orcid":false,"given":"Alireza","family":"Sanaeifar","sequence":"first","affiliation":[{"name":"Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1079-118X","authenticated-orcid":false,"given":"Ce","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA"}]},{"given":"An","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA"}]},{"given":"Colin R.","family":"Jones","sequence":"additional","affiliation":[{"name":"Department of Horticultural Science, University of Minnesota, 1970 Folwell Ave, Saint Paul, MN 55108, USA"}]},{"given":"Thomas E.","family":"Michaels","sequence":"additional","affiliation":[{"name":"Department of Horticultural Science, University of Minnesota, 1970 Folwell Ave, Saint Paul, MN 55108, USA"}]},{"given":"Quinton J.","family":"Krueger","sequence":"additional","affiliation":[{"name":"Verilytix Inc., 2975 Klondike Avenue N, Lake Elmo, MN 55042, USA"}]},{"given":"Robert","family":"Barnes","sequence":"additional","affiliation":[{"name":"Verilytix Inc., 2975 Klondike Avenue N, Lake Elmo, MN 55042, USA"}]},{"given":"Toby J.","family":"Velte","sequence":"additional","affiliation":[{"name":"Verilytix Inc., 2975 Klondike Avenue N, Lake Elmo, MN 55042, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1111\/gcbb.12779","article-title":"Fertility Management for Industrial Hemp Production: Current Knowledge and Future Research Needs","volume":"13","author":"Wylie","year":"2021","journal-title":"GCB Bioenergy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Adesina, I., Bhowmik, A., Sharma, H., and Shahbazi, A. (2020). A Review on the Current State of Knowledge of Growing Conditions, Agronomic Soil Health Practices and Utilities of Hemp in the United States. Agriculture, 10.","DOI":"10.3390\/agriculture10040129"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2134\/cftm2015.0159","article-title":"Industrial Hemp Response to Nitrogen, Phosphorus, and Potassium Fertilization","volume":"1","author":"Aubin","year":"2015","journal-title":"Crop Forage Turfgrass Manag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cockson, P., Landis, H., Smith, T., Hicks, K., and Whipker, B.E. (2019). Characterization of Nutrient Disorders of Cannabis sativa. Appl. Sci., 9.","DOI":"10.3390\/app9204432"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"616672","DOI":"10.3389\/fpls.2020.616672","article-title":"Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review","volume":"11","author":"Payne","year":"2021","journal-title":"Front. Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"160652","DOI":"10.1016\/j.scitotenv.2022.160652","article-title":"Proximal Hyperspectral Sensing of Abiotic Stresses in Plants","volume":"861","author":"Sanaeifar","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113056","DOI":"10.1016\/j.ecoenv.2021.113056","article-title":"Study on Effects of Airborne Pb Pollution on Quality Indicators and Accumulation in Tea Plants Using Vis-NIR Spectroscopy Coupled with Radial Basis Function Neural Network","volume":"229","author":"Sanaeifar","year":"2022","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2018.02.003","article-title":"Close-Range Hyperspectral Image Analysis for the Early Detection of Stress Responses in Individual Plants in a High-Throughput Phenotyping Platform","volume":"138","author":"Mishra","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105459","DOI":"10.1016\/j.compag.2020.105459","article-title":"Modern Imaging Techniques in Plant Nutrition Analysis: A Review","volume":"174","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"147758","DOI":"10.1016\/j.scitotenv.2021.147758","article-title":"Monitoring Natural and Anthropogenic Plant Stressors by Hyperspectral Remote Sensing: Recommendations and Guidelines Based on a Meta-Review","volume":"788","author":"Lassalle","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Weksler, S., Rozenstein, O., Haish, N., Moshelion, M., Walach, R., and Ben-Dor, E. (2020). A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance. Remote Sens., 12.","DOI":"10.3390\/rs12091493"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Siedliska, A., Baranowski, P., Pastuszka-Wo\u017aniak, J., Zubik, M., and Krzyszczak, J. (2021). Identification of Plant Leaf Phosphorus Content at Different Growth Stages Based on Hyperspectral Reflectance. BMC Plant Biol., 21.","DOI":"10.1186\/s12870-020-02807-4"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Pinheiro, M.M.F., Moriya, \u00c9.A.S., Imai, N.N., Estrabis, N., Ianczyk, F., de Ara\u00fajo, F.F., Liesenberg, V., and de Castro Jorge, L.A. (2020). A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12060906"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Holmes, W.S., Po-Leen Ooi, M., Kuang, Y.C., Simpkin, R., Lopez-Ubiria, I., Vidiella, A., Blanchon, D., Gupta, G.S., and Demidenko, S. (2020, January 25\u201328). Classifying Cannabis sativa Flowers, Stems and Leaves Using Statistical Machine Learning with Near-Infrared Hyperspectral Reflectance Imaging. Proceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia.","DOI":"10.1109\/I2MTC43012.2020.9129531"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pereira, J.F.Q., Pimentel, M.F., Amigo, J.M., and Honorato, R.S. (2020). Detection and Identification of Cannabis sativa L. Using near Infrared Hyperspectral Imaging and Machine Learning Methods. A Feasibility Study. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 237.","DOI":"10.1016\/j.saa.2020.118385"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"810113","DOI":"10.3389\/fpls.2021.810113","article-title":"Hyperspectral Imaging With Machine Learning to Differentiate Cultivars, Growth Stages, Flowers, and Leaves of Industrial Hemp (Cannabis sativa L.)","volume":"12","author":"Lu","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107387","DOI":"10.1016\/j.compag.2022.107387","article-title":"Hyperspectral Imaging with Chemometrics for Non-Destructive Determination of Cannabinoids in Floral and Leaf Materials of Industrial Hemp (Cannabis sativa L.)","volume":"202","author":"Lu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1133505","DOI":"10.3389\/fpls.2023.1133505","article-title":"A Hyperspectral Plant Health Monitoring System for Space Crop Production","volume":"14","author":"Qin","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1080\/10106049.2020.1768593","article-title":"A Comparative Analysis of Different Phenological Information Retrieved from Sentinel-2 Time Series Images to Improve Crop Classification: A Machine Learning Approach","volume":"37","author":"Htitiou","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.biosystemseng.2021.08.016","article-title":"A Data Fusion Approach on Confocal Raman Microspectroscopy and Electronic Nose for Quantitative Evaluation of Pesticide Residue in Tea","volume":"210","author":"Sanaeifar","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"132680","DOI":"10.1016\/j.snb.2022.132680","article-title":"Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics for Black Tea Quality Assessment during Its Fermentation Process","volume":"373","author":"Luo","year":"2022","journal-title":"Sens. Actuators B Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neucom.2020.01.097","article-title":"Double Robust Principal Component Analysis","volume":"391","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, H.D., Yun, Y.H., Zhang, W., Chen, H., Liu, D., Zhong, Q., Chen, W., and Chen, W. (2020). Three-Step Hybrid Strategy towards Efficiently Selecting Variables in Multivariate Calibration of near-Infrared Spectra. Spectrochim. Acta A Mol. Biomol. Spectrosc., 224.","DOI":"10.1016\/j.saa.2019.117376"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"999877","DOI":"10.3389\/fnut.2022.999877","article-title":"Evaluation of Near-Infrared Hyperspectral Imaging for the Assessment of Potato Processing Aptitude","volume":"9","author":"Arazuri","year":"2022","journal-title":"Front. Nutr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"149824","DOI":"10.1016\/j.scitotenv.2021.149824","article-title":"Rapid Quantitative Characterization of Tea Seedlings under Lead-Containing Aerosol Particles Stress Using Vis-NIR Spectra","volume":"802","author":"Sanaeifar","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"128866","DOI":"10.1016\/j.foodchem.2020.128866","article-title":"Building Robust Models for Identification of Adulteration in Olive Oil Using FT-NIR, PLS-DA and Variable Selection","volume":"345","author":"Vieira","year":"2021","journal-title":"Food Chem."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kong, W., Zhang, C., Cao, F., Liu, F., Luo, S., Tang, Y., and He, Y. (2018). Detection of Sclerotinia Stem Rot on Oilseed Rape (Brassica napus L.) Leaves Using Hyperspectral Imaging. Sensors, 18.","DOI":"10.3390\/s18061764"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.talanta.2015.10.073","article-title":"Prediction of Banana Quality Indices from Color Features Using Support Vector Regression","volume":"148","author":"Sanaeifar","year":"2016","journal-title":"Talanta"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aissou, G., Benouadah, S., El Alami, H., and Kaabouch, N. (2022, January 26\u201329). Instance-Based Supervised Machine Learning Models for Detecting GPS Spoofing Attacks on UAS. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC54503.2022.9720888"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral Image Analysis Techniques for the Detection and Classification of the Early Onset of Plant Disease and Stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zubler, A.V., and Yoon, J.Y. (2020). Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning. Biosensors, 10.","DOI":"10.3390\/bios10120193"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.biosystemseng.2017.09.009","article-title":"Close Range Hyperspectral Imaging of Plants: A Review","volume":"164","author":"Mishra","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, K., Sun, Z., Zhao, F., Yang, T., Tian, Z., Lai, J., Zhu, W., and Long, B. (2021). Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis Ofwinter Wheat Salt Stress. Remote Sens., 13.","DOI":"10.3390\/rs13020250"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1094\/MPMI-12-12-0288-CR","article-title":"Guarding the Green: Pathways to Stomatal Immunity","volume":"26","author":"Sawinski","year":"2013","journal-title":"Mol. Plant-Microbe Interact."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"129141","DOI":"10.1016\/j.foodchem.2021.129141","article-title":"Simultaneous Quantification of Chemical Constituents in Matcha with Visible-near Infrared Hyperspectral Imaging Technology","volume":"350","author":"Ouyang","year":"2021","journal-title":"Food Chem."},{"key":"ref_36","first-page":"6008305","article-title":"Enhance Tensor RPCA-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection","volume":"19","author":"Ao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, B., Wang, T., Bonni, A., and Zhao, G. (2020). Robust Principal Component Analysis for Accurate Outlier Sample Detection in RNA-Seq Data. BMC Bioinform., 21.","DOI":"10.1186\/s12859-020-03608-0"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"da Mata, M.M., Rocha, P.D., de Farias, I.K.T., da Silva, J.L.B., Medeiros:, E.P., Silva, C.S., and da Silva Sim\u00f5es, S. (2022). Distinguishing Cotton Seed Genotypes by Means of Vibrational Spectroscopic Methods (NIR and Raman) and Chemometrics. Spectrochim. Acta A Mol. Biomol. Spectrosc., 266.","DOI":"10.1016\/j.saa.2021.120399"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1109\/ACCESS.2019.2961742","article-title":"Steady-State Process Fault Detection for Liquid Rocket Engines Based on Convolutional Auto-Encoder and One-Class Support Vector Machine","volume":"8","author":"Zhu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1007\/978-3-030-52246-9_51","article-title":"OCR Post Processing Using Support Vector Machines","volume":"Volume 1229","author":"Taghva","year":"2020","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100097","DOI":"10.1016\/j.atech.2022.100097","article-title":"Rapid Classification of Tef [Eragrostis Tef (Zucc.) Trotter] Grain Varieties Using Digital Images in Combination with Multivariate Technique","volume":"3","author":"Asefa","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11119-020-09727-1","article-title":"Predicting Leaf Nitrogen Content in Olive Trees Using Hyperspectral Data for Precision Agriculture","volume":"22","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"17360","DOI":"10.1038\/s41598-020-73745-2","article-title":"Dissection of Hyperspectral Reflectance to Estimate Nitrogen and Chlorophyll Contents in Tea Leaves Based on Machine Learning Algorithms","volume":"10","author":"Yamashita","year":"2020","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:38:29Z","timestamp":1760103509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,2]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010187"],"URL":"https:\/\/doi.org\/10.3390\/rs16010187","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,2]]}}}