{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T13:00:55Z","timestamp":1778936455670,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016711","name":"North Dakota Soybean Council","doi-asserted-by":"publisher","award":["FAR0025454"],"award-info":[{"award-number":["FAR0025454"]}],"id":[{"id":"10.13039\/100016711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["ND01481"],"award-info":[{"award-number":["ND01481"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms.<\/jats:p>","DOI":"10.3390\/rs12244143","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"4143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5552-562X","authenticated-orcid":false,"given":"Oveis","family":"Hassanijalilian","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Boulevard, Fargo, ND 58102, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8884-7959","authenticated-orcid":false,"given":"C.","family":"Igathinathane","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Boulevard, Fargo, ND 58102, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7631-698X","authenticated-orcid":false,"given":"Sreekala","family":"Bajwa","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Boulevard, Fargo, ND 58102, USA"},{"name":"College of Agriculture &amp; Montana Agricultural Experiment Station, Montana State University, 202 Linfield Hall, Bozeman, MT 59717, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Nowatzki","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, North Dakota State University, 1221 Albrecht Boulevard, Fargo, ND 58102, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,18]]},"reference":[{"key":"ref_1","unstructured":"ASA (2020, December 17). 2019 SOYSTATS A Reference Guide to Soybean Facts and Figures. Available online: https:\/\/soygrowers.com."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11104-013-1842-6","article-title":"Morpho-physiological parameters affecting iron deficiency chlorosis in soybean (Glycine max L.)","volume":"374","author":"Vasconcelos","year":"2014","journal-title":"Plant Soil"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.2134\/agronj2006.0096","article-title":"Iron deficiency chlorosis in soybean","volume":"98","author":"Naeve","year":"2006","journal-title":"Agron. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.2136\/sssaj2010.0391","article-title":"Soil nitrate is a causative factor in iron deficiency chlorosis in soybeans","volume":"75","author":"Bloom","year":"2011","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1081\/PLN-120024257","article-title":"Fe chelates for remediation of Fe chlorosis in strategy I plants","volume":"26","author":"Lucena","year":"2003","journal-title":"J. Plant Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s11104-012-1246-z","article-title":"Evaluation of Fe-N, N\u2032-Bis (2-hydroxybenzyl) ethylenediamine-N, N\u2032-diacetate (HBED\/Fe3+) as Fe carrier for soybean (Glycine max) plants grown in calcareous soil","volume":"360","author":"Nadal","year":"2012","journal-title":"Plant Soil"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.2134\/agronj2000.9261135x","article-title":"A comparison of three methods for reducing iron-deficiency chlorosis in soybean","volume":"92","author":"Goos","year":"2000","journal-title":"Agron. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.2134\/agronj2003.1595","article-title":"Iron deficiency of soybean in the upper Midwest and associated soil properties","volume":"95","author":"Hansen","year":"2003","journal-title":"Agron. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"808","DOI":"10.2134\/agronj2005.0281","article-title":"Genotype\u00d7 environment interactions within iron deficiency chlorosis-tolerant soybean genotypes","volume":"98","author":"Naeve","year":"2006","journal-title":"Agron. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.2134\/agronj13.0296","article-title":"Comparison of field management strategies for preventing iron deficiency chlorosis in soybean","volume":"106","author":"Kaiser","year":"2014","journal-title":"Agron. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"492","DOI":"10.2134\/agronj2009.0317","article-title":"Soybean iron-deficiency chlorosis tolerance and yield decrease on calcareous soils","volume":"102","author":"Helms","year":"2010","journal-title":"Agron. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"619","DOI":"10.2134\/agronj1984.00021962007600040027x","article-title":"Assessment of Visual Evaluation Techniques 1","volume":"76","author":"Horst","year":"1984","journal-title":"Agron. J."},{"key":"ref_13","unstructured":"Van Den Broek, E.L., Vuurpijl, L.G., Kisters, P., and Von Schmid, J.C.M. (2002, January 6). Content-based image retrieval: Color-selection exploited. Proceedings of the Third Dutch-Belgian Information Retrieval Workshop, DIR 2002, Leuven, Belgium."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"943","DOI":"10.2135\/cropsci2003.9430","article-title":"Quantifying turfgrass color using digital image analysis","volume":"43","author":"Karcher","year":"2003","journal-title":"Crop. Sci."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"104854","DOI":"10.1016\/j.compag.2019.104854","article-title":"In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse","volume":"163","author":"Atefi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.2135\/cropsci2010.12.0699","article-title":"The assessment of leaf nitrogen in corn from digital images","volume":"51","author":"Rorie","year":"2011","journal-title":"Crop. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105433","DOI":"10.1016\/j.compag.2020.105433","article-title":"Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning","volume":"174","author":"Hassanijalilian","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"637","DOI":"10.2134\/agronj2015.0222","article-title":"Identifying Field Attributes that Predict Soybean Yield Using Random Forest Analysis","volume":"108","author":"Smidt","year":"2016","journal-title":"Agron. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.compag.2017.12.032","article-title":"Evaluation of support vector machine and artificial neural networks in weed detection using shape features","volume":"145","author":"Bakhshipour","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.compag.2018.11.016","article-title":"Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques","volume":"156","author":"Azadbakht","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, N., Liu, D., Feng, Q., Xiong, Q., Zhang, L., Ren, T., Zhao, Y., Zhu, D., and Huang, J. (2019). Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids. Remote Sens., 11.","DOI":"10.3390\/rs11121500"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2016.10.005","article-title":"Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform","volume":"187","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.3389\/fpls.2018.01002","article-title":"Field-based scoring of soybean iron deficiency chlorosis using RGB imaging and statistical learning","volume":"9","author":"Bai","year":"2018","journal-title":"Front. Plant Sci."},{"key":"ref_26","unstructured":"MATLAB (2015). Version 8.6 (R2015b), The MathWorks Inc.. Image Processing Toolbox."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_28","unstructured":"G\u00e9ron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019 Reilly Media, Inc."},{"key":"ref_29","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Domingos, P. (1999, January 15\u201318). Metacost: A general method for making classifiers cost-sensitive. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA.","DOI":"10.1145\/312129.312220"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.ins.2017.12.030","article-title":"Using generative adversarial networks for improving classification effectiveness in credit card fraud detection","volume":"479","author":"Fiore","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1007\/s10916-019-1402-6","article-title":"Cervical Cancer Identification with Synthetic Minority Oversampling Technique and PCA Analysis using Random Forest Classifier","volume":"43","author":"Geetha","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.compag.2018.05.007","article-title":"Machine learning for automatic rule classification of agricultural regulations: A case study in Spain","volume":"150","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104857","DOI":"10.1016\/j.compag.2019.104857","article-title":"Record linkage for farm-level data analytics: Comparison of deterministic, stochastic and machine learning methods","volume":"163","author":"Aiken","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compag.2014.01.011","article-title":"Predicting shellfish farm closures using time series classification for aquaculture decision support","volume":"102","author":"Shahriar","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","first-page":"431","article-title":"Understanding variable importances in forests of randomized trees","volume":"26","author":"Louppe","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.compag.2018.12.006","article-title":"Current and future applications of statistical machine learning algorithms for agricultural machine vision systems","volume":"156","author":"Rehman","year":"2019","journal-title":"Comput. Electron. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:05Z","timestamp":1760179625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,18]]},"references-count":38,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12244143"],"URL":"https:\/\/doi.org\/10.3390\/rs12244143","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,18]]}}}