{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T13:20:10Z","timestamp":1779196810729,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Fund","award":["31971785"],"award-info":[{"award-number":["31971785"]}]},{"name":"the National Key Research and Development Program","award":["2016YFD0200600-2016YFD0200602"],"award-info":[{"award-number":["2016YFD0200600-2016YFD0200602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI\u2013OTSU\u2013CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE\u2013PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field.<\/jats:p>","DOI":"10.3390\/s20123430","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T13:11:32Z","timestamp":1592399492000},"page":"3430","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1129-9071","authenticated-orcid":false,"given":"Ning","family":"Liu","sequence":"first","affiliation":[{"name":"Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China"},{"name":"Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China"},{"name":"Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China"},{"name":"Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1016\/S2095-3119(17)61736-2","article-title":"Progress of potato staple food research and industry development in China","volume":"16","author":"Hong","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1080\/15427528.2011.538465","article-title":"Improving Nutrient-Use Efficiency in Chinese Potato Production: Experiences from the United States","volume":"25","author":"Alva","year":"2011","journal-title":"J. Crop Improv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s11099-016-0677-9","article-title":"Kaleita. Feasibility of using smart phones to estimate chlorophyll content in corn plants","volume":"55","author":"Vesali","year":"2017","journal-title":"Photosynthetica"},{"key":"ref_4","first-page":"100235","article-title":"Chlorophyll estimation using multi-spectral unmanned aerial system based on machine learning techniques","volume":"15","author":"Gaurav","year":"2019","journal-title":"Remote Sens. Appl.: Soc. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.compag.2017.12.005","article-title":"Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato","volume":"144","author":"Zhou","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/JSTARS.2011.2176468","article-title":"Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content","volume":"5","author":"Clevers","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1007\/s11119-013-9333-6","article-title":"Evaluation of the nitrogen sufficiency index for use with high resolution, broadband aerial imagery in a commercial potato field","volume":"15","author":"Nigon","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_8","first-page":"1323","article-title":"A new image processing-based technique to determine chlorophyll in plants","volume":"12","author":"Ali","year":"2012","journal-title":"Am.-Eurasian J. Agricult. Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jag.2015.01.003","article-title":"Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands","volume":"38","author":"Kira","year":"2015","journal-title":"International J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","first-page":"2930","article-title":"Potato spectrum and the digital image feature parameters on the response of the nitrogen level and its application","volume":"36","author":"He","year":"2016","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Cao, Q., Zhang, K., Ata-Ul-Karim, S.T., Tian, Y., Zhu, Y., Cao, W., and Liu, X. (2016). Optimal Leaf Positions for SPAD Meter Measurement in Rice. Front. Plant Sci., 7.","DOI":"10.3389\/fpls.2016.00719"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Adamsen, F.J., Pinter, P.J., Barnes, E.M., LaMorte, R.L., Wall, G.W., Leavitt, S., and Kimball, B.A. (1999). Measuring wheat senescence with a digital camera. Crop Sci., 719\u2013724.","DOI":"10.2135\/cropsci1999.0011183X003900030019x"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s11120-006-9077-5","article-title":"Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings","volume":"91","author":"Uddling","year":"2007","journal-title":"Photosynth. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"69","DOI":"10.21273\/HORTSCI.27.1.69","article-title":"Inherent Limitations of Nondestructive Chlorophyll Meters: A Comparison of Two Types of Meters","volume":"27","author":"Monje","year":"1992","journal-title":"HortScience"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s11120-010-9606-0","article-title":"Use of a SPAD-502 meter to measure leaf chlorophyll concentration in Arabidopsis thaliana","volume":"107","author":"Ling","year":"2011","journal-title":"Photosynth. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/BF02361796","article-title":"Hand-held chlorophyll meter: A promising tool to assess the nitrogen status of potato foliage","volume":"36","author":"Vos","year":"1993","journal-title":"Potato Res."},{"key":"ref_17","first-page":"798","article-title":"Response of Surface Mulching of Potato (Solanum tuberosum) on SPAD Value, Colorado Potato Beetle and Tuber Yield","volume":"15","year":"2013","journal-title":"Int. J. Agric. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.plaphy.2015.10.032","article-title":"Estimating leaf SPAD values of freeze-damaged winter wheat using continuous wavelet analysis","volume":"98","author":"Wang","year":"2016","journal-title":"Plant Physiol. Biochem."},{"key":"ref_19","first-page":"149","article-title":"Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging","volume":"34","author":"Sun","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_20","first-page":"178","article-title":"Research on Maize Multispectral Image Accurate Segmentation and Chlorophyll Index Estimation","volume":"35","author":"Qian","year":"2015","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_21","first-page":"275","article-title":"Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves","volume":"4","author":"Borhan","year":"2017","journal-title":"Inf. Process. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105173","DOI":"10.1016\/j.compag.2019.105173","article-title":"A low-cost smartphone controlled sensor based on image analysis for estimating whole-plant tissue nitrogen (N) content in floriculture crops","volume":"169","author":"Ranjeeta","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.agrformet.2008.12.007","article-title":"Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices","volume":"149","author":"Wu","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","first-page":"602","article-title":"Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Position","volume":"51","author":"Tao","year":"2018","journal-title":"IFAC Pap."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6270","DOI":"10.3390\/s110606270","article-title":"Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination","volume":"11","author":"Jeon","year":"2011","journal-title":"Sensors"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compag.2016.04.024","article-title":"A survey of image processing techniques for plant extraction and segmentation in the field","volume":"125","author":"Esmael","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.compag.2003.08.002","article-title":"Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images","volume":"42","author":"Meyer","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11119-005-6787-1","article-title":"Automated Crop and Weed Monitoring in Widely Spaced Cereals","volume":"7","author":"Hague","year":"2006","journal-title":"Precis. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compag.2010.09.013","article-title":"Automatic segmentation of relevant textures in agricultural images","volume":"75","author":"Guijarro","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.agrformet.2013.02.011","article-title":"Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage","volume":"174","author":"Yu","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compag.2013.04.010","article-title":"Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model","volume":"96","author":"Guo","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11149","DOI":"10.1016\/j.eswa.2012.03.040","article-title":"Support Vector Machines for crop\/weeds identification in maize fields","volume":"39","author":"Guerrero","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.compag.2008.08.002","article-title":"Mean-shift-based color segmentation of images containing green vegetation","volume":"65","author":"Zheng","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.patrec.2010.01.016","article-title":"Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant","volume":"31","author":"Zheng","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.csda.2014.07.008","article-title":"Consistent and asymptotically normal PLS estimators for linear structural equations","volume":"81","author":"Dijkstra","year":"2015","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.chemolab.2018.03.003","article-title":"libPLS: An integrated library for partial least squares regression and linear discriminant analysis","volume":"176","author":"Li","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.aca.2017.06.001","article-title":"Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination","volume":"982","author":"Andries","year":"2017","journal-title":"Anal. Chim. Acta"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3851","DOI":"10.1021\/ac960321m","article-title":"Elimination of Uninformative Variables for Multivariate Calibration","volume":"68","author":"Centner","year":"1996","journal-title":"Anal. Chem."},{"key":"ref_41","first-page":"1581","article-title":"Detection of Crop Chlorophyll Content Based on Spectrum Extraction from Coating Imaging Sensor","volume":"40","author":"Hong","year":"2020","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial Least-Squares Regression: A Tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, Z., Sakuno, Y., Koike, K., and Ohara, S. (2018). Evaluation of Chlorophyll-a Estimation Approaches Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression and Several Traditional Algorithms from Field Hyperspectral Measurements in the Seto Inland Sea, Japan. Sensors, 18.","DOI":"10.3390\/s18082656"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.infrared.2018.10.030","article-title":"Weighted SPXY method for calibration set selection for composition analysis based on near-infrared spectroscopy","volume":"95","author":"Han","year":"2018","journal-title":"Infrared Phys. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3430\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:07Z","timestamp":1760175607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3430"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,17]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123430"],"URL":"https:\/\/doi.org\/10.3390\/s20123430","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,17]]}}}