Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (19): 3857-3871.doi: 10.3864/j.issn.0578-1752.2025.19.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Comprehensive Assessment of Wheat Seedling Growth Status Based on Multimodal Data

SHAO MingChao(), AN JingWei, LIU BoRui, WU JianShuang, ZHANG Qi, YAO Xia, CHENG Tao, JIANG ChongYa, CAO WeiXing, ZHENG HengBiao(), ZHU Yan()   

  1. College of Agriculture, Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/ Engineering Research Center of Smart Agriculture, Ministry of Education/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095
  • Received:2025-03-25 Accepted:2025-07-23 Online:2025-10-01 Published:2025-10-10
  • Contact: ZHENG HengBiao, ZHU Yan

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Abstract:

【Objective】The seedling condition of wheat reflects the growth status and health level of the seedlings, which is an important basis for yield prediction and field management. Traditional seedling condition evaluation methods relying on manual expertise have limitations in large-scale field applications, such as low efficiency, strong subjectivity, and difficulty in generalization. This study used UAVs equipped with RGB sensors, combined with ground-measured agronomic parameters, to explore a comprehensive wheat seedling condition assessment method that integrated multimodal remote sensing features, so as to provide a technical pathway for large-scale and cross-regional monitoring.【Method】Multi-site and multi-altitude UAV flight tests were designed in Jiangsu Province to capture UAV imagery and to simultaneously collect agronomic parameters, such as tiller number and canopy coverage. Based on vegetation indices and texture features, crop spectral and structural information was extracted. Feature selection was performed using Information Value (IV) and GINI coefficients. Various machine learning models, including random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), were developed to assess the classification accuracy of seedling condition. The optimal image resolution was determined based on the local variance coefficient to enhance the stability and cross-regional adaptability of the application.【Result】The enhanced green-red difference index (EXGR) showed the best accuracy in identifying canopy coverage during the seedling stage (Pixel Accuracy (PA)=0.69, Specificity (S)=0.83). The green-red ratio index (GRRI) exhibited a significant correlation with tiller number (R2=0.58, relative root mean square error (rRMSE)=0.28). The Random Forest algorithm, which integrates agronomic parameters and remote sensing features, achieved the highest accuracy in seedling condition grade classification (PA=0.85, R=0.86). Tiller number and texture information (E_energy) contributed the most to the seedling condition grade classification (IV>0.70). A flight altitude of (35±5) m was found to be an important reference for obtaining high-quality UAV data (local variance=0.17).【Conclusion】This study constructed a comprehensive wheat seedling condition assessment framework integrating agronomic parameters and remote sensing features, which demonstrated the feasibility and efficiency of portable UAV RGB imagery combined with machine learning methods for cross-regional seedling condition grade monitoring. This method could provide data support and methodological references for regional-scale dynamic seedling condition assessment, crop management strategy formulation, and food security assurance.

Key words: seedling condition assessment, UAV RGB imagery, agronomic parameters, random forest, multimodal remote sensing features, wheat

Fig. 1

Location of the study area"

Table 1

Vegetable indexes used in this study"

植被指数 Vegetation index 公式 Formula
绿红比指数 Green-red ration index (GRRI) G/R
绿蓝比指数 Green-blue ration index (GBRI) G/B
蓝红比指数 Blue-red ration index (BRRI) B/R
过红指数 Excess red (EXR) 1.4×R-G
过绿指数 Excess green (EXG) 2×G-R-B
过绿-过红指数 Excess green minus excess red (EXGR) EXG-EXR
植被颜色指数 Color index of vegetation (CIVE) 0.441×r-0.881×g+0.385×b+18.7874
修正超绿指数 Modified excess green (MEXG) 1.262×g-0.884×r-0.311×b
红绿蓝植被指数 Red green blue vegetation Index (RGBVI) (G×G-B×R)/(G×G+B×R)
归一化红绿差异指数Normalized green-red difference index (NGBDI) (G-B)/(G+B)
可见光差异植被指数 Visible band difference vegetation index (VDVI) (2×G-R-B)/(2×G+R+B)

Table 2

Accuracy of canopy cover detection"

植被指数
Vegetation index		 像素精度
Pixel accuracy		 召回率
Recall		 F1分数
F1 score		 特异性
Specificity		 交并比
Intersection over union
EXGR 0.69 0.69 0.70 0.83 0.47
CIVE 0.59 0.68 0.56 0.76 0.41
EXG 0.68 0.66 0.61 0.86 0.46
EXR 0.50 0.71 0.55 0.65 0.39
MEXG 0.58 0.65 0.55 0.77 0.40

Fig. 2

Results of the seedling canopy cover detection"

Fig. 3

Estimation results of tiller number"

Fig. 4

The importance of features"

Table 3

The accuracy of seedling condition detection"

苗情等级 Class 精度指标 Metrics 随机森林 RF 极端梯度提升 XGBOOST 梯度提升决策树 GBDT 决策树 DT
1 像素精度 PA 0.90 1.00 0.95 0.88
召回率 R 1.00 0.32 0.95 0.79
2 像素精度 PA 0.83 0.57 0.86 0.70
召回率 R 0.94 0.64 0.86 0.83
3 像素精度 PA 0.83 0.57 0.80 0.73
召回率 R 0.77 0.18 0.84 0.55
4 像素精度 PA 0.82 0.40 0.92 0.65
召回率 R 0.79 0.81 0.81 0.79
5 像素精度 PA 0.89 0.82 0.79 0.83
召回率 R 0.81 0.67 0.90 0.71

Fig. 5

AUC curve of random forest classifier"

Fig. 6

Monitoring results at different heights"

Fig. 7

The result of seedling condition detecting"

Fig. 8

Wheat seedling condition change chart"

Fig. 9

EXGR image profile"

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