{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:18:34Z","timestamp":1774444714996,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003624","name":"Ministry of Agriculture, Food and Rural Affairs (MAFRA)","doi-asserted-by":"publisher","award":["421030-04"],"award-info":[{"award-number":["421030-04"]}],"id":[{"id":"10.13039\/501100003624","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Strawberry (Fragaria \u00d7 ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm\u2013900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyll-fluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants\u2019 early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSRb), and the red-edge chlorophyll index (CIREDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage.<\/jats:p>","DOI":"10.3390\/s22228706","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:30:52Z","timestamp":1668400252000},"page":"8706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Identification of Early Heat and Water Stress in Strawberry Plants Using Chlorophyll-Fluorescence Indices Extracted via Hyperspectral Images"],"prefix":"10.3390","volume":"22","author":[{"given":"Mangalraj","family":"Poobalasubramanian","sequence":"first","affiliation":[{"name":"Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea"}]},{"given":"Eun-Sung","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea"}]},{"given":"Mohammad Akbar","family":"Faqeerzada","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4478-667X","authenticated-orcid":false,"given":"Taehyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Agriculture Engineering, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54875, Korea"}]},{"given":"Moon Sung","family":"Kim","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1044-349X","authenticated-orcid":false,"given":"Insuck","family":"Baek","sequence":"additional","affiliation":[{"name":"Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Road, BARC-East, Bldg 303, Beltsville, MD 20705, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8397-9853","authenticated-orcid":false,"given":"Byoung-Kwan","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea"},{"name":"Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.15835\/nbha4119009","article-title":"Heat-Stress Tolerance of Some Strawberry (Fragaria \u00d7 Ananassa) Cultivars","volume":"41","author":"Kesici","year":"2013","journal-title":"Not. Bot. Horti Agrobot. Cluj-Napoca"},{"key":"ref_2","first-page":"137","article-title":"The Effect of Climate Change on Abiotic Plant Stress: A Review","volume":"32","author":"Medina","year":"1989","journal-title":"Intech"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.21273\/HORTSCI.41.6.1423","article-title":"Strawberry (Fragaria Xananassa Duch.) Growth and Productivity as Affected by Temperature","volume":"41","author":"Kadir","year":"2006","journal-title":"HortScience"},{"key":"ref_4","first-page":"159","article-title":"Morphological and Physiological Responses of Strawberry Plants to Water Stress","volume":"71","author":"Klamkowski","year":"2006","journal-title":"Agric. Conspec. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.envexpbot.2009.12.001","article-title":"Strawberry Plant Fruiting Efficiency and Its Correlation with Solar Irradiance, Temperature and Reflectance Water Index Variation","volume":"68","author":"Li","year":"2010","journal-title":"Environ. Exp. Bot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/15538362.2022.2114056","article-title":"Shading Reduces Water Deficits in Strawberry (Fragaria X Ananassa) Plants during Vegetative Growth","volume":"22","author":"Magnitskiy","year":"2022","journal-title":"Int. J. Fruit Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11099-010-0040-5","article-title":"Chlorophyll Fluorescence as a Tool for Evaluation of Viability in Freeze-Stressed Grapevine Buds","volume":"48","author":"Zulini","year":"2010","journal-title":"Photosynthetica"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.scienta.2006.10.006","article-title":"Water Relations and Yield of Lysimeter-Grown Strawberries under Limited Irrigation","volume":"111","author":"Liu","year":"2007","journal-title":"Sci. Hortic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.scienta.2007.12.010","article-title":"Effect of High Temperature Stress on the Reproductive Growth of Strawberry Cvs. \u201cNyoho\u201d and \u201cToyonoka\u201d","volume":"116","author":"Ledesma","year":"2008","journal-title":"Sci. Hortic."},{"key":"ref_10","unstructured":"Abdelrahman, M. (1984). Growth and Productivity of Strawberry Cultivars at High Temperatures (Heat Stress, Fragaria), Kansas State University."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hasanuzzaman, M. (2020). Plant Ecophysiology and Adaptation under Climate Change: Mechanisms and Perspectives II: Mechanisms of Adaptation and Stress Amelioration, Springer Nature.","DOI":"10.1007\/978-981-15-2172-0"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jfoodeng.2006.10.016","article-title":"Hyperspectral Imaging for Nondestructive Determination of Some Quality Attributes for Strawberry","volume":"81","author":"ElMasry","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2020). Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability. Remote Sens., 12.","DOI":"10.3390\/rs12010113"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1002\/ppp3.10080","article-title":"Hyperspectral Assessment of Plant Responses to Multi-Stress Environments: Prospects for Managing Protected Agrosystems","volume":"2","author":"Cotrozzi","year":"2020","journal-title":"Plants People Planet"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Park, E., Kim, Y.S., Omari, M.K., Suh, H.K., Faqeerzada, M.A., Kim, M.S., Baek, I., and Cho, B.K. (2021). High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax Ginseng Meyer) Using a Hyperspectral Reflectance Image. Sensors, 21.","DOI":"10.3390\/s21165634"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103898","DOI":"10.1016\/j.infrared.2021.103898","article-title":"Hyperspectral Imaging for Early Identification of Strawberry Leaves Diseases with Machine Learning and Spectral Fingerprint Features","volume":"118","author":"Jiang","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1007\/s13580-014-0006-9","article-title":"Chlorophyll Fluorescence as a Diagnostic Tool for Abiotic Stress Tolerance in Wild and Cultivated Strawberry Species","volume":"55","author":"Na","year":"2014","journal-title":"Hortic. Environ. Biotechnol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aasen, H., Van Wittenberghe, S., Medina, N.S., Damm, A., Goulas, Y., Wieneke, S., Hueni, A., Malenovsk\u00fd, Z., Alonso, L., and Pacheco-Labrador, J. (2019). Sun-Induced Chlorophyll Fluorescence II: Review of Passive Measurement Setups, Protocols, and Their Application at the Leaf to Canopy Level. Remote Sens., 11.","DOI":"10.3390\/rs11080927"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"335","DOI":"10.5307\/JBE.2015.40.4.335","article-title":"Detecting Drought Stress in Soybean Plants Using Hyperspectral Fluorescence Imaging","volume":"40","author":"Mo","year":"2015","journal-title":"J. Biosyst. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.3390\/s110403765","article-title":"Hyperspectral and Chlorophyll Fluorescence Imaging to Analyse the Impact of Fusarium Culmorum on the Photosynthetic Integrity of Infected Wheat Ears","volume":"11","author":"Bauriegel","year":"2011","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"858","DOI":"10.3390\/rs1040858","article-title":"Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves","volume":"1","author":"Delalieux","year":"2009","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, T., Nakano, A., Iwaski, Y., and Umeda, H. (2020). Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories. Appl. Sci., 10.","DOI":"10.3390\/app10134665"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, N., Wang, G., Yang, G., and Dai, W. (2009, January 4\u20136). A Fast 2D Otsu Thresholding Algorithm Based on Improved Histogram. Proceedings of the 2009 Chinese Conference on Pattern Recognition, (CCPR 2009) and the First CJK Joint Workshop on Pattern Recognition (CJKPR), Nanjing, China.","DOI":"10.1109\/CCPR.2009.5344078"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/S0034-4257(99)00048-6","article-title":"Relationships between Spectral Reflectance and Pigment Concentrations in Stacks of Deciduous Broadleaves","volume":"70","author":"Blackburn","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_26","first-page":"663","article-title":"Optimized Carrier Tracking Loop Design for Real-Time High-Dynamics GNSS Receivers","volume":"50","author":"Roncagliolo","year":"2012","journal-title":"Int. J. Navig. Obs."},{"key":"ref_27","unstructured":"Huntington, J., Miller, J.J., Merton, R., and Huntington, J. (1996, January 4\u20138). Early simulation results of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. Proceedings of the Eighth Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"974638","DOI":"10.1155\/2012\/974638","article-title":"Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Nave Bayes Models","volume":"2012","author":"Pradhan","year":"2012","journal-title":"Math. Probl. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:22Z","timestamp":1760145262000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,11]]},"references-count":28,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228706"],"URL":"https:\/\/doi.org\/10.3390\/s22228706","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,11]]}}}