IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v15y2025i3p311-d1580864.html
   My bibliography  Save this article

A Model Combining Sensitive Vegetation Indices and Fractional-Order Differential Characteristic Bands for SPAD Value Estimation in Cd-Contaminated Rice Leaves

Author

Listed:
  • Rongcai Tian

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Bin Zou

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Chinese Ministry of Education, Changsha 410083, China)

  • Shenxin Li

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Li Dai

    (Hunan Rice Research Institute, Changsha 410125, China)

  • Bo Zhang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Yulong Wang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Hao Tu

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Jie Zhang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)

  • Lunwen Zou

    (School of Geographical Sciences, Hunan Normal University, Changsha 410081, China)

Abstract

Rapid and nondestructive estimation of leaf SPAD values is crucial for monitoring the effects of cadmium (Cd) stress in rice. To address the issue of low estimation accuracy in leaf SPAD value models due to the loss of spectral information in existing studies, a new estimation model, which combines sensitive vegetation indices (VIss) and fractional order differential characteristic bands (FODcb), is proposed in this study. To validate the effectiveness of this new model, three scenarios, with no Cd contamination, 1.0 mg/kg Cd contamination, and 1.4 mg/kg Cd contamination, were set up. Leaf spectral reflectance and SPAD values were measured during the critical growth period of rice. Subsequently, 16 vegetation indices were constructed, and fractional order difference (FOD) transformation was applied to process the spectral data. The variable importance in projection (VIP) algorithm was employed to extract VIss and FODcb. Finally, the random forest (RF) algorithm was used to construct three models, VIss + FODcb-RF, FODcb-RF, and VIss-RF. The estimated leaf SPAD values for the three models showed that: (1) there was a significant difference between the leaf SPAD values with no Cd contamination and those treated with 1.4 mg/kg Cd contamination on the 31st and 87th days after transplanting; (2) the 400–773 nm spectral range was sensitive for estimating leaf SPAD values, with the Cd-contaminated scenario exhibiting higher reflectance in the visible wavelength range than the Cd-uncontaminated scenario; (3) compared with the individual FODcb-RF and Viss-RF models, the combined model (VIss + FODcb-RF) improved the estimation accuracy of the leaf SPAD values. Particularly, the Viss + FOD 1.2cb -RF model provided the best performance, with R 2 v, RMSEv, and RPDv values of 0.821, 2.621, and 2.296, respectively. In conclusion, this study demonstrates the effectiveness of combining VIss and FODcb for accurately estimating Cd-contaminated rice leaf SPAD values. This finding will provide a methodological reference for remote sensing monitoring of Cd contamination in rice.

Suggested Citation

  • Rongcai Tian & Bin Zou & Shenxin Li & Li Dai & Bo Zhang & Yulong Wang & Hao Tu & Jie Zhang & Lunwen Zou, 2025. "A Model Combining Sensitive Vegetation Indices and Fractional-Order Differential Characteristic Bands for SPAD Value Estimation in Cd-Contaminated Rice Leaves," Agriculture, MDPI, vol. 15(3), pages 1-20, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:311-:d:1580864
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/15/3/311/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/15/3/311/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xintao Yuan & Xiao Zhang & Nannan Zhang & Rui Ma & Daidi He & Hao Bao & Wujun Sun, 2023. "Hyperspectral Estimation of SPAD Value of Cotton Leaves under Verticillium Wilt Stress Based on GWO–ELM," Agriculture, MDPI, vol. 13(9), pages 1-23, September.
    2. Shuangya Wen & Nan Shi & Junwei Lu & Qianwen Gao & Wenrui Hu & Zhengdengyuan Cao & Jianxiang Lu & Huibin Yang & Zhiqiang Gao, 2022. "Continuous Wavelet Transform and Back Propagation Neural Network for Condition Monitoring Chlorophyll Fluorescence Parameters Fv/Fm of Rice Leaves," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    3. Magali J. López-Calderón & Juan Estrada-Ávalos & Víctor M. Rodríguez-Moreno & Jorge E. Mauricio-Ruvalcaba & Aldo R. Martínez-Sifuentes & Gerardo Delgado-Ramírez & Enrique Miguel-Valle, 2020. "Estimation of Total Nitrogen Content in Forage Maize ( Zea mays L.) Using Spectral Indices: Analysis by Random Forest," Agriculture, MDPI, vol. 10(10), pages 1-15, October.
    4. Nan Lin & Xunhu Ma & Ranzhe Jiang & Menghong Wu & Wenchun Zhang, 2024. "Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm," Agriculture, MDPI, vol. 14(5), pages 1-21, April.
    5. Li Fan & Shibo Fang & Jinlong Fan & Yan Wang & Linqing Zhan & Yongkun He, 2024. "Rice Yield Estimation Using Machine Learning and Feature Selection in Hilly and Mountainous Chongqing, China," Agriculture, MDPI, vol. 14(9), pages 1-18, September.
    6. Hanhu Liu & Xiangqi Lei & Hui Liang & Xiao Wang, 2023. "Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ying Nian & Xiangxiang Su & Hu Yue & Sumera Anwar & Jun Li & Weiqiang Wang & Yali Sheng & Qiang Ma & Jikai Liu & Xinwei Li, 2024. "Winter Wheat SPAD Prediction Based on Multiple Preprocessing, Sequential Module Fusion, and Feature Mining Methods," Agriculture, MDPI, vol. 14(12), pages 1-21, December.
    2. Tazeem Haider & Muhammad Shahid Farid & Rashid Mahmood & Areeba Ilyas & Muhammad Hassan Khan & Sakeena Tul-Ain Haider & Muhammad Hamid Chaudhry & Mehreen Gul, 2021. "A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves," Agriculture, MDPI, vol. 11(8), pages 1-19, August.
    3. Nita Yuniati & Kusumiyati Kusumiyati & Syariful Mubarok & Bambang Nurhadi, 2023. "Assessment of Biostimulant Derived from Moringa Leaf Extract on Growth, Physiology, Yield, and Quality of Green Chili Pepper," Sustainability, MDPI, vol. 15(9), pages 1-13, April.
    4. Monistrol, Alba & Vallejo, Antonio & García-Gutiérrez, Sandra & Hermoso-Peralo, Roberto & Montoya, Mónica & Atencia-Payares, Luz K. & Aguilera, Eduardo & Guardia, Guillermo, 2024. "Interaction between burial depth and N source in drip-fertigated maize: Agronomic performance and correlation with spectral indices," Agricultural Water Management, Elsevier, vol. 301(C).
    5. Peng Huang & Pan Yang & Libiao Yang & Futong Xiao & Yanqi Feng & Yuchao Wang, 2024. "Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning," Agriculture, MDPI, vol. 14(12), pages 1-18, December.
    6. Srinivasagan N. Subhashree & C. Igathinathane & John Hendrickson & David Archer & Mark Liebig & Jonathan Halvorson & Scott Kronberg & David Toledo & Kevin Sedivec, 2025. "Evaluating Remote Sensing Resolutions and Machine Learning Methods for Biomass Yield Prediction in Northern Great Plains Pastures," Agriculture, MDPI, vol. 15(5), pages 1-24, February.
    7. Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data," Sustainability, MDPI, vol. 13(21), pages 1-21, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:15:y:2025:i:3:p:311-:d:1580864. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.