{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:24:52Z","timestamp":1767338692590,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brno University of Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.<\/jats:p>","DOI":"10.3390\/rs15123152","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T08:56:01Z","timestamp":1686905761000},"page":"3152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9940-4966","authenticated-orcid":false,"given":"Ji\u0159\u00ed","family":"Janou\u0161ek","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7349-8426","authenticated-orcid":false,"given":"Petr","family":"Marco\u0148","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic"}]},{"given":"P\u0159emysl","family":"Dohnal","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic"}]},{"given":"V\u00e1clav","family":"Jambor","sequence":"additional","affiliation":[{"name":"NutriVet s.r.o., V\u00edde\u0148sk\u00e1 1023, 69123 Poho\u0159elice, Czech Republic"}]},{"given":"Hana","family":"Synkov\u00e1","sequence":"additional","affiliation":[{"name":"NutriVet s.r.o., V\u00edde\u0148sk\u00e1 1023, 69123 Poho\u0159elice, Czech Republic"}]},{"given":"Petr","family":"Raichl","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Janou\u0161ek, J., Jambor, V., Marco\u0148, P., Dohnal, P., Synkov\u00e1, H., and Fiala, P. (2021). Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops. Remote Sens., 13.","DOI":"10.3390\/rs13101878"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mina\u0159\u00edk, R., Langhammer, J., and Lendzioch, T. (2021). Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13234768"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6978","DOI":"10.1007\/s13197-015-1838-8","article-title":"Non-invasive hyperspectral imaging approach for fruit quality control application and classification: Case study of apple, chikoo, guava fruits","volume":"52","author":"Vetrekar","year":"2015","journal-title":"J. Food Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1111\/j.1745-4530.2004.00464.x","article-title":"Multispectral imaging system for fecal and igesta detection on poultry carcasses","volume":"27","author":"Park","year":"2004","journal-title":"J. Food Process Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.postharvbio.2015.09.027","article-title":"Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification","volume":"111","author":"Cen","year":"2016","journal-title":"Postharvest Biol. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.jfoodeng.2009.01.028","article-title":"Multispectral images of peach related to firmness and maturity at harvest","volume":"93","author":"Barreiro","year":"2009","journal-title":"J. Food Eng."},{"key":"ref_7","first-page":"1779","article-title":"Classification of Fungal Infected Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Support Vector Machine","volume":"50","author":"Zhang","year":"2007","journal-title":"Trans. ASABE Am. Soc. Agric. Biol. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lukas, V., Hu\u0148ady, I., Kintl, A., Mezera, J., Hammerschmiedt, T., Sobotkov\u00e1, J., Brtnick\u00fd, M., and Elbl, J. (2022). Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage. Remote Sens., 14.","DOI":"10.3390\/rs14194953"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Duffkov\u00e1, R., Pol\u00e1kov\u00e1, L., Lukas, V., and Fu\u010d\u00edk, P. (2022). The Effect of Controlled Tile Drainage on Growth and Grain Yield of Spring Barley as Detected by UAV Images, Yield Map and Soil Moisture Content. Remote Sens., 14.","DOI":"10.3390\/rs14194959"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gracia-Romero, A., Vergara-D\u00edaz, O., Thierfelder, C., Cairns, J.E., Kefauver, S.C., and Araus, J.L. (2018). Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrid Performance in Zimbabwe. Proceedings, 2.","DOI":"10.3390\/ecrs-2-05181"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, B., Zhu, W., Rezaei, E.E., Li, J., Sun, Z., and Zhang, J. (2022). The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14071559"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.3390\/rs70302971","article-title":"Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture","volume":"7","author":"Matese","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","first-page":"100549","article-title":"Efficient Maize Tassel-Detection Method using UAV based remote sensing","volume":"23","author":"Kumar","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Lei, L., Qiu, C., Li, Z., Han, D., Han, L., Zhu, Y., Wu, J., Xu, B., Feng, H., and Yang, H. (2019). Effect of Leaf Occlusion on Leaf Area Index Inversion of Maize Using UAV\u2013LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11091067"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Louis, J., Pflug, B., Main-Knorn, M., Debaecker, V., Mueller-Wilm, U., Iannone, R.Q., Cadau, E.G., Boccia, V., and Gascon, F. (August, January 28). Sentinel-2 Global Surface Reflectance Level-2a Product Generated with Sen2Cor. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898540"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2362","DOI":"10.2135\/cropsci2013.10.0710","article-title":"Prediction of Dry Matter Yield of Hybrid Forage Corn Grown for Silage","volume":"54","author":"Islam","year":"2014","journal-title":"Crop Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4374","DOI":"10.1109\/JSTARS.2014.2334332","article-title":"Analysis of NDVI Data for Crop Identification and Yield Estimation","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/10106040508542350","article-title":"Relationships between NDVI, Grassland Production, and Crop Yield in the Central Great Plains","volume":"20","author":"Wang","year":"2005","journal-title":"Geocarto Int."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.rse.2014.03.008","article-title":"Near real-time prediction of U.S. corn yields based on time-series MODIS data","volume":"147","author":"Sakamoto","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.rse.2013.07.018","article-title":"Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction","volume":"138","author":"Ines","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.10.027","article-title":"An assessment of pre- and in-season remotely sensed variables for forecasting corn and soybean yields in the United States","volume":"141","author":"Johnson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112408","DOI":"10.1016\/j.rse.2021.112408","article-title":"Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach","volume":"259","author":"Ma","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"de Oliveira, M.F., Ortiz, B.V., Morata, G.T., Jim\u00e9nez, A.-F., Rolim, G.D.S., and da Silva, R.P. (2022). Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction. Remote Sens., 14.","DOI":"10.3390\/rs14236171"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tang, Z., Guo, J., Xiang, Y., Lu, X., Wang, Q., Wang, H., Cheng, M., Wang, H., Wang, X., and An, J. (2022). Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index. Agronomy, 12.","DOI":"10.3390\/agronomy12071729"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N.H., and Islam, N. (2022). A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14091990"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop Yield Prediction Using Machine Learning: A Systematic Literature Review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.procs.2021.04.146","article-title":"Neural Network for Grain Yield Predicting Based Multispectral Satellite Imagery: Comparative Study","volume":"186","author":"Khalil","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10922","DOI":"10.1016\/j.matpr.2021.01.948","article-title":"Analysis of Agricultural Crop Yield Prediction Using Statistical Techniques of Machine Learning","volume":"46","author":"Pant","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ilyas, Q., Ahmad, M., and Mehmood, A. (2023). Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020125"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, R., Cherkauer, K., and Bowling, L. (2016). Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8040269"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2004.12.006","article-title":"Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA\u2019s-AVHRR","volume":"129","author":"Mkhabela","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","first-page":"109","article-title":"Vegetation indices in the prediction of biomass and grain yield of white oat under irrigation levels","volume":"48","author":"Coelho","year":"2018","journal-title":"Trop. Agric. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1111\/gfs.12517","article-title":"Prediction of aboveground biomass and dry-matter content in Brachiaria pastures by combining meteorological data and satellite imagery","volume":"76","author":"Bretas","year":"2021","journal-title":"Grass Forage Sci."},{"key":"ref_38","first-page":"816767","article-title":"Spectral Indices: In-Season Dry Mass and Yield Relationship of Flue-Cured Tobacco under Different Planting Dates and Fertiliser Levels","volume":"2013","author":"Svotwa","year":"2013","journal-title":"Int. Sch. Res. Not."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105236","DOI":"10.1016\/j.compag.2020.105236","article-title":"Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing","volume":"169","author":"Maresma","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","first-page":"1","article-title":"Relationship between normalized difference vegetation index (NDVI) and forage biomass yield in the Vakinankaratra region, Madagascar","volume":"26","author":"Rahetlah","year":"2014","journal-title":"Livest. Res. Rural. Dev."},{"key":"ref_41","first-page":"71","article-title":"Evaluation of leaf area index and dry matter predictions for crop growth modelling and yield estimation based on field reflectance measurements","volume":"14","author":"Gerighausen","year":"2016","journal-title":"eARSeL eProceedings"},{"key":"ref_42","first-page":"1","article-title":"Evaluation of The Nutritional Status of Corn by Vegetation Indices Via Aerial Images","volume":"51","author":"Junior","year":"2021","journal-title":"Ci\u00eancia Rural"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"de Souza, R., Pe\u00f1a-Fleitas, M.T., Thompson, R.B., Gallardo, M., and Padilla, F.M. (2020). Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper. Remote Sens., 12.","DOI":"10.3390\/rs12050763"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.14393\/BJ-v36n5a2020-47993","article-title":"Relationship between Vegetation Indices and Agronomic Performance of Maize Varieties under Different Nitrogen Rates","volume":"36","author":"Flores","year":"2020","journal-title":"Biosci. J."},{"key":"ref_45","first-page":"1","article-title":"On Correlation between Canopy Vegetation and Growth Indexes of Maize Varieties with Different Nitrogen Efficiencies","volume":"18","author":"Zhao","year":"2023","journal-title":"Open Life Sci."},{"key":"ref_46","first-page":"1","article-title":"Vegetation Indices for Crop Management: A Review","volume":"6","author":"Janse","year":"2019","journal-title":"Int. J. Res. Anal. Rev. IJRAR"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0034-4257(94)00111-Y","article-title":"Leaf area index estimation from visible and near-infrared reflectance data","volume":"52","author":"Price","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Buma, W.G., and Lee, S.-I. (2019). Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa. Remote Sens., 11.","DOI":"10.3390\/rs11212534"},{"key":"ref_51","unstructured":"Corrigan, F. (2020, March 22). Multispectral Imaging Camera Drones in Farming Yield Big Benefits, DroneZon, Available online: https:\/\/www.dronezon.com\/learn-about-drones-quadcopters\/multispectral-sensor-drones-in-farming-yield-big-benefits\/."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.2134\/agronj2011.0124","article-title":"Use of the Canopy Chlorophyll Content Index (CCCI) for Remote Estimation of Wheat Nitrogen Content in Rainfed Environments","volume":"103","author":"Cammarano","year":"2011","journal-title":"Agron. J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.biosystemseng.2008.09.011","article-title":"Quickbird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots","volume":"101","author":"Bausch","year":"2008","journal-title":"Biosyst. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote sensing of chlorophyll concentration in higher plant leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_55","unstructured":"\u00daKZ\u00daZ (1999). Methods of Plant Variety State Tests CISTA, Pursuant to the Valid Wording from the Year 1999, \u00daKZ\u00daZ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1093\/pan\/2.1.173","article-title":"What Does \u201cExplained Variance\u201d Explain? A Reply","volume":"2","author":"Achen","year":"1990","journal-title":"Political Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:56:35Z","timestamp":1760126195000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,16]]},"references-count":56,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123152"],"URL":"https:\/\/doi.org\/10.3390\/rs15123152","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,6,16]]}}}