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Field-Scale Maize Yield Estimation Using Remote Sensing with the Integration of Agronomic Traits

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  • Shuai Bao

    (College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing 163000, China
    State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Yiang Wang

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Shinai Ma

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Huanjun Liu

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Xiyu Xue

    (College of Horticulture and Landscape Architecture, Northeast Agriculture University, Harbin 150030, China)

  • Yuxin Ma

    (State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Mingcong Zhang

    (College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing 163000, China
    Key Laboratory of Low-Carbon Green Agriculture in Northeastern China, College of Horticulture and Landscape Architecture, Northeast Agriculture University, Daqing 163000, China)

  • Dianyao Wang

    (Heilongjiang Youyi Green Agriculture Science and Technology Field Station, Shuangyashan 155800, China)

Abstract

Maize ( Zea mays L.) is a key global cereal crop with significant relevance to food security. Maize yield prediction is challenged by cultivar diversity and varying management practices. This preliminary study was conducted at Youyi Farm, Heilongjiang Province, China. Three maize cultivars (Songyu 438, Dika 1220, Dika 2188), two fertilization rates (700 and 800 kg·ha −1 ), and three planting densities (70,000, 75,000, and 80,000 plants·ha −1 ) were evaluated across 18 distinct cropping treatments. During the V6 (Vegetative 6-leaf stage), VT (Tasseling stage), R3 (Milk stage), and R6 (Physiological maturity) growth stages of maize, multi-temporal canopy spectral images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. In situ measurements of key agronomic traits, including plant height (PH), stem diameter (SD), leaf area index (LAI), and relative chlorophyll content (SPAD), were conducted. The optimal vegetation indices (VIs) and agronomic traits were selected for developing a maize yield prediction model using the random forest (RF) algorithm. Results showed the following: (1) Vegetation indices derived from the red-edge band, particularly the normalized difference red-edge index (NDRE), exhibited a strong correlation with maize yield (R = 0.664), especially during the tasseling to milk ripening stage; (2) The integration of LAI and SPAD with NDRE improved model performance, achieving an R 2 of 0.69—an increase of 23.2% compared to models based solely on VIs; (3) Incorporating SPAD values from middle-canopy leaves during the milk ripening stage further enhanced prediction accuracy (R 2 = 0.74, RMSE = 0.88 t·ha −1 ), highlighting the value of vertical-scale physiological parameters in yield modeling. This study not only furnishes critical technical support for the application of UAV-based remote sensing in precision agriculture at the field-plot scale, but also charts a clear direction for the synergistic optimization of multi-dimensional agronomic traits and spectral features.

Suggested Citation

  • Shuai Bao & Yiang Wang & Shinai Ma & Huanjun Liu & Xiyu Xue & Yuxin Ma & Mingcong Zhang & Dianyao Wang, 2025. "Field-Scale Maize Yield Estimation Using Remote Sensing with the Integration of Agronomic Traits," Agriculture, MDPI, vol. 15(17), pages 1-23, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:17:p:1834-:d:1736824
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    1. Héctor García-Martínez & Héctor Flores-Magdaleno & Roberto Ascencio-Hernández & Abdul Khalil-Gardezi & Leonardo Tijerina-Chávez & Oscar R. Mancilla-Villa & Mario A. Vázquez-Peña, 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-24, July.
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