Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network

Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different n...

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Vydáno v:Journal of agricultural engineering (Pisa, Italy) Ročník 54; číslo 4
Hlavní autoři: Zhao, Jiangui, Zhu, Tingyu, Qiu, Zhichao, Li, Tao, Wang, Guoliang, Li, Zhiwei, Du, Huiling
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bologna PAGEPress Publications 01.01.2023
Témata:
Algorithms
Back propagation
Back propagation networks
Carotenoids
Chlorophyll
Convergence
Fertilization
Hyperspectral imaging
Infrared imaging
Leaves
logistic chaotic mapping
Low concentrations
Machine learning
Mapping
Neural networks
Nitrogen
Nutrient concentrations
Nutrient content
Pigments
pigments content
Predictions
Root-mean-square errors
Search algorithms
Seedlings
sparrow search algorithm
tomato leaf
Tomatoes
ISSN:1974-7071, 2239-6268
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Abstract Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.
AbstractList Leaf pigment content can reflect the nutrient content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from the leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared hyperspectral imaging non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on logistic chaotic mapping was proposed, and it optimized the back propagation neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of the leaves reached its maximum. Meanwhile, the inhibition effect of high concentrations was much stronger than that of low concentrations. To address the problem that the SSA is prone to premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by logistic chaotic mapping improves the initial solution quality, convergence speed, and search capacity. The root mean squared error (RMSE), coefficient of determition (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77, and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66, and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81, and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75, and 2.00, respectively. Hyperspectral imaging technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise magement of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.
Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato leaves, chlorophyll a, chlorophyll b, chlorophyll and carotenoid were extracted from leaves of tomato seedlings cultured at different nitrogen concentrations. The visible/near-infrared(VIS/NIR) hyperspectral imaging (HSI) non-destructive measurement technology, 430-900 nm and 950-1650 nm, with total variables of 794, was used to obtain the reflection spectra of leaves. An improved strategy of the sparrow search algorithm (SSA) based on Logistic chaotic mapping was proposed and optimized the back propagation (BP) neural network to predict the pigment content of leaves. Different pretreatment methods were used to effectively improve the prediction accuracy of the model. The results showed that when the nitrogen concentration in the nutrient solution was 302.84 mg·L-1, the pigment content of leaves reached the maximum. Meanwhile, the inhibition effect of high concentration was much stronger than that of low concentration. To address the problem that the SSA is prone to get in premature convergence due to the reduction of population diversity at the end of the iteration, the initialization of the SSA population by Logistic chaotic mapping improves the initial solution quality, convergence speed and search capacity. The root mean squared error (RMSE), coefficient of determination (R2) and relative percent deviation (RPD) of chlorophyll a were 0.77, 0.77 and 2.08, respectively. The RMSE, R2 and RPD of chlorophyll b were 0.30, 0.66 and 1.71, respectively. The RMSE, R2 and RPD of chlorophyll were 0.88, 0.81 and 2.28, respectively. The RMSE, R2 and RPD of carotenoid were 0.14, 0.75 and 2.00, respectively. The HSI technology combined with machine learning algorithms can achieve rapid and accurate prediction of crop physiological information, providing data support for the precise management of fertilization in facility agriculture, which is conducive to improving the quality and output of tomatoes.
Author Li, Tao
Zhu, Tingyu
Qiu, Zhichao
Zhao, Jiangui
Wang, Guoliang
Du, Huiling
Li, Zhiwei
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CitedBy_id crossref_primary_10_1007_s11769_024_1421_1
crossref_primary_10_1016_j_foodchem_2025_143913
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Snippet Leaf pigment content can reflect the nutrient elements content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of...
Leaf pigment content can reflect the nutrient content of the cultivation medium indirectly. To rapidly and accurately predict the pigment content of tomato...
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SubjectTerms Algorithms
Back propagation
Back propagation networks
Carotenoids
Chlorophyll
Convergence
Fertilization
Hyperspectral imaging
Infrared imaging
Leaves
logistic chaotic mapping
Low concentrations
Machine learning
Mapping
Neural networks
Nitrogen
Nutrient concentrations
Nutrient content
Pigments
pigments content
Predictions
Root-mean-square errors
Search algorithms
Seedlings
sparrow search algorithm
tomato leaf
Tomatoes
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Title Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network
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