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

Published: 20 June 2023
Abstract Views: 632
PDF: 202
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


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 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. Hyperspectral imaging 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.



PlumX Metrics


Download data is not yet available.


Baglieri A, Cadili V, Monterumici C M, et al. 2014. Fertilization of bean plants with tomato plants hydrolysates. Effect on biomass production, chlorophyll content and N assimilation. SCI HORTIC-AMSTERDAM. 176: 194-199. DOI: https://doi.org/10.1016/j.scienta.2014.07.002
Fathy A, Alanazi T M, Rezk H, et al. 2022. Optimal energy management of micro-grid using sparrow search algorithm. ENERGY R. 8: 758-773. DOI: https://doi.org/10.1016/j.egyr.2021.12.022
Flores P, Carvajal M, Cerdá A, et al. 2001. Salinity and ammonium/nitrate interactions on tomato plant development, nutrition, and metabolites. J PLANT NUTR. 24: 1561-1573. DOI: https://doi.org/10.1081/PLN-100106021
Fontes P C R, Pereira P R G, Conde R M. 1997. Critical chlorophyll, total nitrogen, and nitrate‐nitrogen in leaves associated to maximum lettuce yield. J PLANT NUTR. 20: 1061-1068. DOI: https://doi.org/10.1080/01904169709365318
Galvao R K H, Araujo M C U, José G E, et al. 2005. A method for calibration and validation subset partitioning. TALANTA. 67: 736-740. DOI: https://doi.org/10.1016/j.talanta.2005.03.025
Gao B, Shen W, Guan H, et al. 2022. Research on multistrategy improved evolutionary sparrow search algorithm and its application. IEEE ACCESS. 10: 62520-62534. DOI: https://doi.org/10.1109/ACCESS.2022.3182241
Huang H, He R, Yao W, et al. 2013. Noncentrosymmetric mixed-cation borate: Crystal growth, structure and optical properties of Cs2Ca[B4O5(OH)4]2·8H2O. J CRYST GROWTH. 380: 176-181. DOI: https://doi.org/10.1016/j.jcrysgro.2013.06.018
Kanso A, Smaoui N. 2009. Logistic chaotic maps for binary numbers generations. CHAOS SOLITON FRACT. 40: 2557-2568. DOI: https://doi.org/10.1016/j.chaos.2007.10.049
Kostin G A, Borodin A O, Mikhailov A A, et al. 2015. Photocrystallographic, Spectroscopic, and Calorimetric Analysis of Light-Induced Linkage NO Isomers in [RuNO(NO2)2(pyridine)2OH]. EUR J INORG CHEM. 2015: 4905-4913. DOI: https://doi.org/10.1002/ejic.201500702
Li J, Shi Y, Veeranampalayam-Sivakumar A N, et al. 2018. Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. FRONT PLANT SCI. 9: 1406. DOI: https://doi.org/10.3389/fpls.2018.01406
Ouyang Q, Wang L, Park B, et al. 2021. Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology. FOOD CHEM. 350: 129141. DOI: https://doi.org/10.1016/j.foodchem.2021.129141
Sankar S, Manikandan M R, Ram S D G, et al. 2010. Gel growth of α and γ glycine and their characterization. J CRYST GROWTH. 312: 2729-2733. DOI: https://doi.org/10.1016/j.jcrysgro.2010.04.051
Schmilovitch Z, Ignat T, Alchanatis V, et al. 2014. Hyperspectral imaging of intact bell peppers. BIOSYST ENG, 117: 83-93. DOI: https://doi.org/10.1016/j.biosystemseng.2013.07.003
Tuerxun W, Chang X, Hongyu G, et al. 2021. Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm. IEEE ACCESS. 9: 69307-69315. DOI: https://doi.org/10.1109/ACCESS.2021.3075547
Wang S, Guan K, Wang Z, et al. 2021. Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy. J EXP BOT. 72: 341-354. DOI: https://doi.org/10.1093/jxb/eraa432
Wang W, Li Z, Wang C, et al. 2019. Prediction of available potassium content in cinnamon soil by hyperspectral imaging. SPECTROSC SPECT ANAL. 39:1579-1585.
Wang Y, Hu X, Jin G, et al. 2019. Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging. J SCI FOOD AGR. 99: 1997-2004. DOI: https://doi.org/10.1002/jsfa.9399
Wellburn A R. 1994. The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. J PLANT PHYSIOL. 144: 307-313. DOI: https://doi.org/10.1016/S0176-1617(11)81192-2
Xu H R, Ying Y B, Fu X P, et al. 2007. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. BIOSYST ENG. 96: 447-454. DOI: https://doi.org/10.1016/j.biosystemseng.2007.01.008
Xue J, Shen B. 2020. A novel swarm intelligence optimization approach: sparrow search algorithm. SYST SCI CONTROL ENG. 8: 22-34. DOI: https://doi.org/10.1080/21642583.2019.1708830
Yan S, Yang P, Zhu D, et al. 2021. Improved sparrow search algorithm based on iterative local search. COMPUT INTEL NEUROSC. 2021. DOI: https://doi.org/10.1155/2021/6860503
Yao J S, Yang H Q, He Y. 2009. Nondestructive detection of rape leaf chlorophyll level based on Vis/NIR spectroscopy. J ZHEJIANG UNIV-SC A. 35: 433-438.
Yuan J, Zhao Z, Liu Y, et al. 2021. DMPPT control of photovoltaic microgrid based on improved sparrow search algorithm. IEEE ACCESS. 9: 16623-16629. DOI: https://doi.org/10.1109/ACCESS.2021.3052960
Zhang D, Lou S. 2021. The application research of neural network and BP algorithm in stock price pattern classification and prediction. FUTURE GENER COMP SY. 115: 872-879. DOI: https://doi.org/10.1016/j.future.2020.10.009
Zhang S, Zhang L, Gai T, et al. 2022. Aberration analysis and compensate method of a BP neural network and sparrow search algorithm in deep ultraviolet lithography. APPL OPTICS. 61: 6023-6032. DOI: https://doi.org/10.1364/AO.462436

How to Cite

Zhao, J., Zhu, T., Qiu, Z., Li, T., Wang, G., Li, Z. and Du, H. (2023) “Hyperspectral prediction of pigment content in tomato leaves based on logistic-optimized sparrow search algorithm and back propagation neural network”, Journal of Agricultural Engineering, 54(4). doi: 10.4081/jae.2023.1528.