Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of frui...

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Published in:	PloS one Vol. 19; no. 8; p. e0308826
Main Authors:	Elmetwalli, Adel H., Derbala, Asaad, Alsudays, Ibtisam Mohammed, Al-Shahari, Eman A., Elhosary, Mahmoud, Elsayed, Salah, Al-Shuraym, Laila A., Moghanm, Farahat S., Elsherbiny, Osama
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 26.08.2024 Public Library of Science (PLoS)
Subjects:	Acidity Algorithms Artificial intelligence Biology and Life Sciences Carotenoids Carotenoids - analysis Carotenoids - metabolism Chlorophyll Chlorophyll - analysis Chlorophyll A - analysis Citrus - growth & development Citrus fruits Computer and Information Sciences Crop diseases Crops Datasets Decision trees Diseases and pests Engineering and Technology Feature selection Food quality Fruit - chemistry Fruit - growth & development Fruits Genetic aspects Growth Hyperspectral Imaging - methods Image processing Image quality Learning algorithms Lycopene Lycopene - analysis Machine Learning Mandarins Methods Nondestructive testing Parameter estimation Performance evaluation Physical Sciences Reflectance Research and Analysis Methods Ripening Solanum lycopersicum - growth & development Spectral reflectance Spectrum analysis Tangerine Tomatoes Vegetables Vegetation index Egypt
ISSN:	1932-6203, 1932-6203
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Abstract	Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R 2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R 2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R 2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710 , 600 , and R 730 , 650 showed the greatest R 2 values with respect to Chl a (R 2 = 0.80) for mandarin fruits while the GI had the greatest R 2 with Chl a (R 2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R 2 of 0.993 with an RMSE of 0.149 for the training set, and an R 2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R 2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R 2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.
AbstractList	Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R.sup.2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R.sup.2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R.sup.2 = 0.84 with car of tomato fruits. The SRIs such as RSI .sub.710,.sub.600, and R.sub.730,.sub.650 showed the greatest R.sup.2 values with respect to Chl a (R.sup.2 = 0.80) for mandarin fruits while the GI had the greatest R.sup.2 with Chl a (R.sup.2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R.sup.2 of 0.993 with an RMSE of 0.149 for the training set, and an R.sup.2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R.sup.2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R.sup.2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R 2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R 2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R 2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710 , 600 , and R 730 , 650 showed the greatest R 2 values with respect to Chl a (R 2 = 0.80) for mandarin fruits while the GI had the greatest R 2 with Chl a (R 2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R 2 of 0.993 with an RMSE of 0.149 for the training set, and an R 2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R 2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R 2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R 2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R 2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R 2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710 , 600 , and R 730 , 650 showed the greatest R 2 values with respect to Chl a (R 2 = 0.80) for mandarin fruits while the GI had the greatest R 2 with Chl a (R 2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R 2 of 0.993 with an RMSE of 0.149 for the training set, and an R 2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R 2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R 2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.
Audience	Academic
Author	Alsudays, Ibtisam Mohammed Al-Shuraym, Laila A. Elsayed, Salah Derbala, Asaad Moghanm, Farahat S. Al-Shahari, Eman A. Elsherbiny, Osama Elhosary, Mahmoud Elmetwalli, Adel H.
AuthorAffiliation	1 Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta, Egypt 5 New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Iraq 4 Evaluation of Natural Resources Department, Agricultural Engineering, Environmental Studies and Research Institute, University of Sadat City, Minufia, Egypt 6 Biology Department, Faculty of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia 7 Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh, Egypt Bahauddin Zakariya University, PAKISTAN 2 Department of Biology, College of Science, Qassim University, Unaizah, Saudi Arabia 3 Department of Biology, Faculty of Science and Arts, King Khalid University, Abha, Saudi Arabia 8 Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, Egypt
AuthorAffiliation_xml	– name: Bahauddin Zakariya University, PAKISTAN – name: 8 Department of Agricultural Engineering, Faculty of Agriculture, Mansoura University, Mansoura, Egypt – name: 3 Department of Biology, Faculty of Science and Arts, King Khalid University, Abha, Saudi Arabia – name: 5 New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Iraq – name: 7 Soil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh, Egypt – name: 2 Department of Biology, College of Science, Qassim University, Unaizah, Saudi Arabia – name: 6 Biology Department, Faculty of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia – name: 4 Evaluation of Natural Resources Department, Agricultural Engineering, Environmental Studies and Research Institute, University of Sadat City, Minufia, Egypt – name: 1 Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta, Egypt
Author_xml	– sequence: 1 givenname: Adel H. surname: Elmetwalli fullname: Elmetwalli, Adel H. – sequence: 2 givenname: Asaad surname: Derbala fullname: Derbala, Asaad – sequence: 3 givenname: Ibtisam Mohammed orcidid: 0000-0002-0632-1281 surname: Alsudays fullname: Alsudays, Ibtisam Mohammed – sequence: 4 givenname: Eman A. surname: Al-Shahari fullname: Al-Shahari, Eman A. – sequence: 5 givenname: Mahmoud surname: Elhosary fullname: Elhosary, Mahmoud – sequence: 6 givenname: Salah surname: Elsayed fullname: Elsayed, Salah – sequence: 7 givenname: Laila A. surname: Al-Shuraym fullname: Al-Shuraym, Laila A. – sequence: 8 givenname: Farahat S. surname: Moghanm fullname: Moghanm, Farahat S. – sequence: 9 givenname: Osama orcidid: 0000-0003-3031-7108 surname: Elsherbiny fullname: Elsherbiny, Osama
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39186505$$D View this record in MEDLINE/PubMed
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CitedBy_id	crossref_primary_10_1007_s11119_025_10269_7 crossref_primary_10_3390_agriengineering7020039 crossref_primary_10_1016_j_jfca_2025_107974 crossref_primary_10_3390_agronomy15092229 crossref_primary_10_1016_j_jfca_2025_108086 crossref_primary_10_3390_f16030394
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ContentType	Journal Article
Copyright	Copyright: © 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2024 Public Library of Science 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2024 Elmetwalli et al 2024 Elmetwalli et al 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: Copyright: © 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: COPYRIGHT 2024 Public Library of Science – notice: 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2024 Elmetwalli et al 2024 Elmetwalli et al – notice: 2024 Elmetwalli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet	Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of...
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SubjectTerms	Acidity Algorithms Artificial intelligence Biology and Life Sciences Carotenoids Carotenoids - analysis Carotenoids - metabolism Chlorophyll Chlorophyll - analysis Chlorophyll A - analysis Citrus - growth & development Citrus fruits Computer and Information Sciences Crop diseases Crops Datasets Decision trees Diseases and pests Engineering and Technology Feature selection Food quality Fruit - chemistry Fruit - growth & development Fruits Genetic aspects Growth Hyperspectral Imaging - methods Image processing Image quality Learning algorithms Lycopene Lycopene - analysis Machine Learning Mandarins Methods Nondestructive testing Parameter estimation Performance evaluation Physical Sciences Reflectance Research and Analysis Methods Ripening Solanum lycopersicum - growth & development Spectral reflectance Spectrum analysis Tangerine Tomatoes Vegetables Vegetation index
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Title	Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies
URI	https://www.ncbi.nlm.nih.gov/pubmed/39186505 https://www.proquest.com/docview/3097454612 https://www.proquest.com/docview/3097491798 https://pubmed.ncbi.nlm.nih.gov/PMC11346661 https://doaj.org/article/321b1bca95e9467a93d48ff5d0669ab8 http://dx.doi.org/10.1371/journal.pone.0308826
Volume	19
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