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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Veröffentlicht in:PloS one Jg. 19; H. 8; S. e0308826
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 26.08.2024
Public Library of Science (PLoS)
Schlagworte:
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
Online-Zugang:Volltext
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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 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.
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.
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.
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  givenname: Asaad
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  givenname: Ibtisam Mohammed
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  givenname: Eman A.
  surname: Al-Shahari
  fullname: Al-Shahari, Eman A.
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  givenname: Mahmoud
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  fullname: Elhosary, Mahmoud
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  givenname: Salah
  surname: Elsayed
  fullname: Elsayed, Salah
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  givenname: Laila A.
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  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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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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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
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