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Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting

Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in...

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Autores principales: Sabzi, Sajad, Pourdarbani, Razieh, Rohban, Mohammad Hossein, Fuentes-Penna, Alejandro, Hernández-Hernández, José Luis, Hernández-Hernández, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146435/
https://www.ncbi.nlm.nih.gov/pubmed/33946780
http://dx.doi.org/10.3390/plants10050898
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author Sabzi, Sajad
Pourdarbani, Razieh
Rohban, Mohammad Hossein
Fuentes-Penna, Alejandro
Hernández-Hernández, José Luis
Hernández-Hernández, Mario
author_facet Sabzi, Sajad
Pourdarbani, Razieh
Rohban, Mohammad Hossein
Fuentes-Penna, Alejandro
Hernández-Hernández, José Luis
Hernández-Hernández, Mario
author_sort Sabzi, Sajad
collection PubMed
description Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.
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spelling pubmed-81464352021-05-26 Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting Sabzi, Sajad Pourdarbani, Razieh Rohban, Mohammad Hossein Fuentes-Penna, Alejandro Hernández-Hernández, José Luis Hernández-Hernández, Mario Plants (Basel) Article Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen. MDPI 2021-04-29 /pmc/articles/PMC8146435/ /pubmed/33946780 http://dx.doi.org/10.3390/plants10050898 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sabzi, Sajad
Pourdarbani, Razieh
Rohban, Mohammad Hossein
Fuentes-Penna, Alejandro
Hernández-Hernández, José Luis
Hernández-Hernández, Mario
Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting
title Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting
title_full Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting
title_fullStr Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting
title_full_unstemmed Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting
title_short Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting
title_sort classification of cucumber leaves based on nitrogen content using the hyperspectral imaging technique and majority voting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146435/
https://www.ncbi.nlm.nih.gov/pubmed/33946780
http://dx.doi.org/10.3390/plants10050898
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