{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T09:22:46Z","timestamp":1781515366318,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,13]],"date-time":"2020-04-13T00:00:00Z","timestamp":1586736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Portable devices for measuring plant physiological features with their isolated measuring chamber are playing an increasingly important role in plant phenotyping. However, currently available commercial devices of this type, such as soil plant analysis development (SPAD) meter and spectrometer, are dot meters that only measure a small region of the leaf, which does not perfectly represent the highly varied leaf surface. This study developed a portable and high-resolution multispectral imager (named LeafScope) to in-vivo image a whole leaf of dicotyledon plants while blocking the ambient light. The hardware system is comprised of a monochrome camera, an imaging chamber, a lightbox with different bands of light-emitting diodes (LEDs) array, and a microcontroller. During measuring, the device presses the leaf to lay it flat in the imaging chamber and acquires multiple images while alternating the LED bands within seconds in a certain order. The results of an experiment with soybean plants clearly showed the effect of nitrogen and water treatments as well as the genotype differences by the color and morphological features from image processing. We conclude that the low cost and easy to use LeafScope can provide promising imaging quality for dicotyledon plants, so it has great potential to be used in plant phenotyping.<\/jats:p>","DOI":"10.3390\/s20082194","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T03:10:01Z","timestamp":1586833801000},"page":"2194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["LeafScope: A Portable High-Resolution Multispectral Imager for In Vivo Imaging Soybean Leaf"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-6145","authenticated-orcid":false,"given":"Liangju","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunhong","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yikai","family":"Li","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering, Purdue University, 315 Grant St., West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, 225 S. University St., West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Baerenfaller, K., Massonnet, C., Walsh, S., Baginsky, S., B\u00fchlmann, P., Hennig, L., Hirsch-Hoffmann, M., Howell, K.A., Kahlau, S., and Radziejwoski, A. (2012). Systems-based analysis of Arabidopsis leaf growth reveals adaptation to water deficit. Mol. Syst. Biol., 8.","DOI":"10.1038\/msb.2012.39"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.tplants.2013.04.008","article-title":"Cell to whole-plant phenotyping: the best is yet to come","volume":"18","author":"Dhondt","year":"2013","journal-title":"Trends Plant Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kumar, P., and Sharma, M.K. (2013). Nutrient deficiencies of field crops: guide to diagnosis and management, Cabi.","DOI":"10.1079\/9781780642789.0000"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4613","DOI":"10.1073\/pnas.1716999115","article-title":"An explainable deep machine vision framework for plant stress phenotyping","volume":"115","author":"Ghosal","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s13007-017-0173-7","article-title":"A real-time phenotyping framework using machine learning for plant stress severity rating in soybean","volume":"13","author":"Naik","year":"2017","journal-title":"Plant Methods"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.compag.2016.11.019","article-title":"Detection of soybean aphids in a greenhouse using an image processing technique","volume":"132","author":"Maharlooei","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3724\/SP.J.1258.2012.00088","article-title":"A review of leaf morphology plasticity linked to plant response and adaptation characteristics in arid ecosystems","volume":"36","author":"Li","year":"2012","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1006\/anbo.2001.1391","article-title":"Evolution and function of leaf venation architecture: A review","volume":"87","author":"Uhl","year":"2001","journal-title":"Ann. Bot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1104\/pp.110.162834","article-title":"Leaf extraction and analysis framework graphical user interface: Segmenting and analyzing the structure of leaf veins and areoles","volume":"155","author":"Price","year":"2011","journal-title":"Plant Physiol."},{"key":"ref_11","first-page":"2359","article-title":"Phenovein\u2014A tool for leaf vein segmentation and analysis","volume":"169","author":"Rishmawi","year":"2015","journal-title":"Plant Physiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13029-017-0064-3","article-title":"NET: a new framework for the vectorization and examination of network data","volume":"12","author":"Lasser","year":"2017","journal-title":"Source Code Biol. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1111\/j.1365-313X.2011.04803.x","article-title":"Quantitative analysis of venation patterns of Arabidopsis leaves by supervised image analysis","volume":"69","author":"Dhondt","year":"2012","journal-title":"Plant J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"15669","DOI":"10.1038\/srep15669","article-title":"NEFI: Network extraction from images","volume":"5","author":"Dirnberger","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.compbiolchem.2019.03.012","article-title":"Automatic hierarchy classification in venation networks using directional morphological filtering for hierarchical structure traits extraction","volume":"80","author":"Gan","year":"2019","journal-title":"Comput. Biol. Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1021\/ac60085a028","article-title":"Kjeldahl Method for Organic Nitrogen","volume":"26","author":"Bradstreet","year":"1954","journal-title":"Anal. Chem."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1104\/pp.77.2.483","article-title":"Extinction coefficients of chlorophyll a and b in N, N -dimethylformamide and 80% acetone","volume":"77","author":"Inskeep","year":"1985","journal-title":"Plant Physiol."},{"key":"ref_18","first-page":"69","article-title":"Measurement of leaf relative water content in Araucaria Angustifolia","volume":"11","author":"Yamasaki","year":"1999","journal-title":"Rev. Bras. De Fisiol. Veg."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1016\/j.rse.2007.12.014","article-title":"Three decades of hyperspectral remote sensing of the Earth: A personal view","volume":"113","author":"Goetz","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2018.12.041","article-title":"Ground based hyperspectral imaging for extensive mango yield estimation","volume":"157","author":"Wendel","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","unstructured":"(2019, September 05). Purdue University Controlled Environment Phenotyping Facility. Available online: https:\/\/ag.purdue.edu\/cepf\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.compag.2016.07.028","article-title":"Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging","volume":"127","author":"Ge","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/BF00032301","article-title":"Calibration of the Minolta SPAD-502 leaf chlorophyll meter","volume":"46","author":"Markwell","year":"1995","journal-title":"Photosynth. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13389","DOI":"10.1038\/srep13389","article-title":"SPAD-based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteristics","volume":"5","author":"Xiong","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s13007-019-0450-8","article-title":"High-throughput analysis of leaf physiological and chemical traits with VIS\u2013NIR\u2013SWIR spectroscopy: a case study with a maize diversity panel","volume":"15","author":"Ge","year":"2019","journal-title":"Plant Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s10661-019-7615-9","article-title":"Automated framework for accurate segmentation of leaf images for plant health assessment","volume":"191","author":"Ghazal","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.compag.2019.04.035","article-title":"Detection of nutrition deficiencies in plants using proximal images and machine learning: A review","volume":"162","author":"Barbedo","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, L., Jin, J., Song, Z., Wang, J., Zhang, L., Rehman, T.U., Ma, D., Carpenter, N.R., and Tuinstra, M.R. (2020). LeafSpec: An accurate and portable hyperspectral corn leaf imager. Comput. Electron. Agric., 169.","DOI":"10.1016\/j.compag.2019.105209"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105069","DOI":"10.1016\/j.compag.2019.105069","article-title":"Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping","volume":"167","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tirado, S.B., Dennis, S.S., Enders, T.A., and Springer, N.M. (2020). Utilizing top-down hyperspectral imaging for monitoring genotype and growth conditions in maize. bioRxiv.","DOI":"10.1101\/2020.01.21.914069"},{"key":"ref_32","first-page":"100","article-title":"Drought stress in plants: A review on morphological characteristics and pigments composition","volume":"11","author":"Jaleel","year":"2009","journal-title":"Int. J. Agric. Biol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13007-018-0274-y","article-title":"Improved non-destructive 2D and 3D X-ray imaging of leaf venation","volume":"14","author":"Schneider","year":"2018","journal-title":"Plant Methods"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:08:56Z","timestamp":1760364536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/8\/2194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,13]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20082194"],"URL":"https:\/\/doi.org\/10.3390\/s20082194","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,13]]}}}