{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T22:31:14Z","timestamp":1775082674687,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"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>Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239).<\/jats:p>","DOI":"10.3390\/s23073756","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T05:42:44Z","timestamp":1680673364000},"page":"3756","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4164-4980","authenticated-orcid":false,"given":"Xuan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziling","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8668-0163","authenticated-orcid":false,"given":"Xing","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianzhang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.tplants.2021.07.015","article-title":"Advances in optical phenotyping of cereal crops","volume":"27","author":"Sun","year":"2021","journal-title":"Trends Plant Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.molp.2020.01.008","article-title":"Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives","volume":"13","author":"Yang","year":"2020","journal-title":"Mol. Plant"},{"key":"ref_3","first-page":"327","article-title":"Applications of Remote and Proximal Sensing for Improved Precision in Forest Operations","volume":"38","author":"Talbot","year":"2017","journal-title":"Croat. J. For. Eng"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"623705","DOI":"10.3389\/fbioe.2020.623705","article-title":"High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field","volume":"8","author":"Li","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_5","first-page":"100782","article-title":"Estimation of soybean grain yield from multispectral high-resolution UAV data with machine learning models in West Africa","volume":"27","author":"Alabi","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gano, B., Dembele, J.S.B., Ndour, A., Luquet, D., Beurier, G., Diouf, D., and Audebert, A. (2021). Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions. Agronomy, 11.","DOI":"10.3390\/agronomy11050850"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"de Oca, A.M., Arreola, L., Flores, A., Sanchez, J., and Flores, G. (2018, January 12\u201315). Low-Cost Multispectral Imaging System for Crop Monitoring. Proceedings of the 2018 International Conference on Unmanned Aircraft Systems, Dallas, TX, USA.","DOI":"10.1109\/ICUAS.2018.8453426"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13007-015-0078-2","article-title":"Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize","volume":"11","author":"Vergara","year":"2015","journal-title":"Plant Methods"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107581","DOI":"10.1016\/j.agwat.2022.107581","article-title":"Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes","volume":"266","author":"Yousfi","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Su, W., Zhang, M., Bian, D., Liu, Z., Huang, J., Wang, W., Wu, J., and Guo, H. (2019). Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images. Remote Sens., 11.","DOI":"10.3390\/rs11172021"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.3389\/fpls.2017.01920","article-title":"High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness","volume":"8","author":"Barmeier","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"904","DOI":"10.3389\/fpls.2019.00904","article-title":"Management and characterization of abiotic stress via ph\u00e9nofield\u00ae a high-throughput field phenotyping platform","volume":"10","author":"Leroy","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1038\/s41598-018-19142-2","article-title":"GPhenoVision: A Ground Mobile System with Multi-modal Imaging for Field-Based High Throughput Phenotyping of Cotton","volume":"8","author":"Jiang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_15","unstructured":"Murman, J.N. (2019). Flex-Ro: A Robotic High Throughput Field Phenotyping System. [Master\u2019s Thesis, University of Nebraska-Lincoln]."},{"key":"ref_16","unstructured":"Lindsey, R. (2009). Climate and Earth\u2019s Energy Budget, NASA Earth Observatory."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.2134\/agronj2007.0322","article-title":"Vertical Profile and Temporal Variation of Chlorophyll in Maize Canopy: Quantitative \u201cCrop Vigor\u201d Indicator by Means of Reflectance-Based Techniques","volume":"100","author":"Ciganda","year":"2008","journal-title":"Agron. J."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1094\/PHYTO-11-16-0417-R","article-title":"Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning","volume":"107","author":"DeChant","year":"2017","journal-title":"Phytopathology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s13007-022-00934-7","article-title":"Automated, image-based disease measurement for phenotyping resistance to soybean frogeye leaf spot","volume":"18","author":"McDonald","year":"2022","journal-title":"Plant Methods"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3146\/0095-3679-43.1.36","article-title":"Phenotyping Peanut Genotypes for Drought Tolerance","volume":"43","author":"Luis","year":"2016","journal-title":"Peanut Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1270\/jsbbs.65.285","article-title":"WIPPER: An accurate and efficient field phenotyping platform for large-scale applications","volume":"65","author":"Utsushi","year":"2015","journal-title":"Breed. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.compag.2010.11.003","article-title":"Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean","volume":"75","author":"Vollmann","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"719","DOI":"10.3389\/fpls.2016.00719","article-title":"Optimal Leaf Positions for SPAD Meter Measurement in Rice","volume":"7","author":"Yuan","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ma, D., Wang, L., Zhang, L., Song, Z., Rehman, T.U., and Jin, J. (2020). Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality. Sensors, 20.","DOI":"10.3390\/s20133659"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.33158\/ASB.r150.v8.2022","article-title":"Symptoms and interrelationships of macro and micronutrients available for soybean","volume":"8","author":"Treter","year":"2021","journal-title":"Agron. Sci. Biotechnol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105209","DOI":"10.1016\/j.compag.2019.105209","article-title":"LeafSpec: An accurate and portable hyperspectral corn leaf imager","volume":"169","author":"Wang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, L., Duan, Y., Zhang, L., Wang, J., Li, Y., and Jin, J. (2020). LeafScope: A Portable High-Resolution Multispectral Imager for In Vivo Imaging Soybean Leaf. Sensors, 20.","DOI":"10.3390\/s20082194"},{"key":"ref_29","unstructured":"Oliveira, M., Bowen, B., McKenna, R., and Chang, Y.M. (2001, January 3\u20135). Fast Digital Image Inpainting. Proceedings of the International Conference on Visualization, Imaging and Image Processing (VIIP 2001), Marbella, Spain. Available online: https:\/\/www.inf.ufrgs.br\/~oliveira\/pubs_files\/inpainting.pdf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1007\/s11063-019-10163-0","article-title":"Image inpainting: A review","volume":"51","author":"Elharrouss","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1556\/CRC.39.2011.1.15","article-title":"NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions","volume":"39","author":"Molero","year":"2011","journal-title":"Cereal Res. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107550","DOI":"10.1016\/j.compag.2022.107550","article-title":"NLCS - A novel coordinate system for spatial analysis on hyperspectral leaf images and an improved nitrogen index for soybean plants","volume":"204","author":"Song","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","unstructured":"USGS, n.d. (2023, February 21). Landsat Normalized Difference Vegetation Index, Available online: https:\/\/www.usgs.gov\/landsat-missions\/landsat-normalized-difference-vegetation-index."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.fcr.2007.03.023","article-title":"Remote sensing of nitrogen and water stress in wheat","volume":"104","author":"Tilling","year":"2007","journal-title":"Field Crop. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.5194\/isprs-archives-XLI-B1-1011-2016","article-title":"Novel approach for estimating nitrogen content in paddy fields using low altitude remote sensing system","volume":"41","author":"Saberioon","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3756\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:10:28Z","timestamp":1760123428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,5]]},"references-count":35,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073756"],"URL":"https:\/\/doi.org\/10.3390\/s23073756","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,5]]}}}