{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:48:04Z","timestamp":1772300884189,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400\u20131100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions.<\/jats:p>","DOI":"10.3390\/rs14246366","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:41:41Z","timestamp":1671439301000},"page":"6366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves"],"prefix":"10.3390","volume":"14","author":[{"given":"Brahim","family":"Benmouna","sequence":"first","affiliation":[{"name":"Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0766-8305","authenticated-orcid":false,"given":"Raziyeh","family":"Pourdarbani","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran"}]},{"given":"Sajad","family":"Sabzi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Sharif University of Technology, Tehran 11155-1639, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-8416","authenticated-orcid":false,"given":"Ruben","family":"Fernandez-Beltran","sequence":"additional","affiliation":[{"name":"Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2521-4454","authenticated-orcid":false,"given":"Gin\u00e9s","family":"Garc\u00eda-Mateos","sequence":"additional","affiliation":[{"name":"Computer Science and Systems Department, University of Murcia, 30100 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8122-5487","authenticated-orcid":false,"given":"Jos\u00e9 Miguel","family":"Molina-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Food Engineering and Agricultural Equipment Department, Technical University of Cartagena, 30203 Cartagena, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","first-page":"176","article-title":"Mineral nutrition of tomato","volume":"1","author":"Sainju","year":"2003","journal-title":"Food Agric. Environ"},{"key":"ref_2","first-page":"186","article-title":"Quality of tomato fertilized with nitrogen and phosphorous","volume":"22","author":"Migliori","year":"2010","journal-title":"Ital. J. Food Sci."},{"key":"ref_3","unstructured":"Srivastava, A.K., and Hu, C. (2020). Chapter 4\u2014Plant nutrition and physiological disorders in fruit crops. Fruit Crops, Elsevier."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Bern\u00e1, J.A., Ouhbi, S., Benmouna, B., Garc\u00eda-Mateos, G., Fern\u00e1ndez-Alem\u00e1n, J.L., and Molina-Mart\u00ednez, J.M. (2020). Systematic mapping study on remote sensing in agriculture. Appl. Sci., 10.","DOI":"10.3390\/app10103456"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.compag.2018.11.005","article-title":"Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers","volume":"156","author":"Pantazi","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_6","first-page":"100473","article-title":"A new Conv2D model with modified ReLU activation function for identification of disease type and severity in cucumber plant","volume":"30","author":"Agarwal","year":"2021","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_7","first-page":"100407","article-title":"Development of Efficient CNN model for Tomato crop disease identification","volume":"28","author":"Agarwal","year":"2020","journal-title":"Sustain. Comput. Inform. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1080\/03650340.2016.1247952","article-title":"Early sensing of peanut leaf spot using spectroscopy and thermal imaging","volume":"63","author":"Omran","year":"2017","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104147","DOI":"10.1016\/j.chemolab.2020.104147","article-title":"Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stages","volume":"206","author":"Pourdarbani","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s11119-017-9507-8","article-title":"Detection of biotic and abiotic stresses in crops by using hierarchical self-organizing classifiers","volume":"18","author":"Pantazi","year":"2017","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s10712-019-09511-5","article-title":"Assessing Vegetation Function with Imaging Spectroscopy","volume":"40","author":"Gamon","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s11119-019-09693-3","article-title":"Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming","volume":"21","author":"Leenen","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.saa.2018.12.051","article-title":"Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging","volume":"212","author":"Jun","year":"2019","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.postharvbio.2019.04.005","article-title":"Pathogenetic process monitoring and early detection of pear black spot disease caused by Alternaria alternata using hyperspectral imaging","volume":"154","author":"Pan","year":"2019","journal-title":"Postharvest Biol. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"118917","DOI":"10.1016\/j.saa.2020.118917","article-title":"Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods","volume":"245","author":"Yu","year":"2021","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compag.2019.02.022","article-title":"Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)","volume":"159","author":"Xia","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TBDATA.2019.2923243","article-title":"Beyond the patchwise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification","volume":"6","author":"Xu","year":"2019","journal-title":"IEEE Trans. Big Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"105066","DOI":"10.1016\/j.compag.2019.105066","article-title":"Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms","volume":"167","author":"Gu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105039","DOI":"10.1016\/j.compag.2019.105039","article-title":"Detection of anthracnose in tea plants based on hyperspectral imaging","volume":"167","author":"Yuan","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","unstructured":"Xu, Y., Du, B., and Zhang, L. (2022). Robust Self-Ensembling Network for Hyperspectral Image Classification, IEEE. IEEE Transactions on Neural Networks and Learning Systems."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.chemolab.2007.06.006","article-title":"Software for chemometric analysis of spectroscopic data","volume":"90","author":"Rossel","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Majumdar, P., Dey, S., Bardhan, S., and Mitra, S. (2022). Support Vector Machines for the Classification of Remote Sensing Images: A Review. Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0, CRC Press.","DOI":"10.1201\/9781003279044-12"},{"key":"ref_24","first-page":"13","article-title":"Support Vector Machines for Classification","volume":"70","author":"Fradkin","year":"2006","journal-title":"DIMACS Ser. Discret. Math. Theor. Comput. Sci."},{"key":"ref_25","unstructured":"Abdi, H. (2007). Discriminant Correspondence Analysis, SAGE."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sabzi, S., Pourdarbani, R., and Arribas, J.I. (2020). A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images. Computers, 9.","DOI":"10.3390\/computers9010006"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pourdarbani, R., Sabzi, S., Garc\u00eda-Amicis, V.M., Garc\u00eda-Mateos, G., Molina-Mart\u00ednez, J.M., and Ruiz-Canales, A. (2019). Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9.","DOI":"10.3390\/agronomy9110672"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1243\/09544062JMES1494","article-title":"The bees algorithm: Modelling foraging behaviour to solve continuous optimization problems","volume":"223","author":"Pham","year":"2009","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Atashpaz-Gargari, E., and Lucas, C. (2007, January 25\u201328). Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, Singapore.","DOI":"10.1109\/CEC.2007.4425083"},{"key":"ref_30","unstructured":"Ragav, V., and Li, B. (2017). Convolutional Neural Networks in Visual Computing: A Concise Guide, CRC Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_33","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sabzi, S., Pourdarbani, R., Rohban, M.H., Garc\u00eda-Mateos, G., Paliwal, J., and Molina-Mart\u00ednez, J.M. (2021). Early detection of excess nitrogen consumption in cucumber plants using hyperspectral imaging based on hybrid neural networks and the imperialist competitive algorithm. Agronomy, 11.","DOI":"10.3390\/agronomy11030575"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.chemolab.2017.12.010","article-title":"Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf","volume":"172","author":"Yu","year":"2018","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.biosystemseng.2017.03.006","article-title":"Hyperspectral imaging of spinach canopy under combined water and nitrogen stress to estimate biomass, water, and nitrogen content","volume":"158","author":"Corti","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-021-00815-5","article-title":"Water and nitrogen in-situ imaging detection in live corn leaves using near-infrared camera and interference filter","volume":"17","author":"Zhang","year":"2021","journal-title":"Plant Methods"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Miphokasap, P., and Wannasiri, W. (2018). Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery. Sustainability, 10.","DOI":"10.3390\/su10041266"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11119-019-09661-x","article-title":"Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging","volume":"21","author":"Ye","year":"2020","journal-title":"Precis. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6366\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:36Z","timestamp":1760146956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":39,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246366"],"URL":"https:\/\/doi.org\/10.3390\/rs14246366","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}