Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (9): 1719-1734.doi: 10.3864/j.issn.0578-1752.2025.09.004

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

Multi-Angle Imaging and Machine Learning Approaches for Accurate Rice Leaf Area Estimation

WANG AiDong1(), LI RuiJie1,2(), FENG XiangQian1,2, HONG WeiYuan1, LI ZiQiu1, ZHANG XiaoGuo1, WANG DanYing1, CHEN Song1()   

  1. 1 China National Rice Research Institute/State Key Laboratory of Rice Biology and Breeding, Hangzhou 311400
    2 College of Agriculture, Yangtze University, Jingzhou 434025, Hubei
  • Received:2024-09-30 Accepted:2024-12-29 Online:2025-05-08 Published:2025-05-08
  • Contact: CHEN Song

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Abstract:

【Objective】 Rice leaf area is a critical physiological metric that indicates photosynthetic efficiency, energy conversion, and dry matter accumulation capacity. This study aimed to develop a simple and efficient rice leaf area imaging system and prediction method, so as to provide a theoretical foundation and technical support for rapid and accurate leaf area measurement.【Method】 The study utilized representative rice varieties—Xiushui 134 (indica), Huanghuazhan (japonica), and Yongyou 1540 (indica-japonica hybrid)—as experimental materials. Leaf area data were collected from the aboveground parts during critical growth periods, and both flat-overhead-view and side-view images were captured. Using the PlantScreen high-throughput modular plant phenotyping platform, morphological and color feature information was extracted. Based on these data, various feature selection methods(Pearson correlation coefficient, maximal information coefficient (MIC), and recursive feature elimination (RFE)) combined with machine learning models (support vector regression (SVR), random forest regression (RFR), and XGBoost) and deep learning models (ResNet50, AlexNet, VGG, and SeNet) were employed to develop a simplified and efficient rice leaf area prediction model.【Result】 (1) An imaging approach that integrated flat-overhead and multi-angle side views significantly outperformed single-view methods for leaf area prediction, with R² values of 0.76-0.82 and coefficients of variation (CV) of 5.5%-13.7%, compared with R² values of 0.51-0.78 and CVs of 9.7%-27.5% for single views. The optimal system used one flat-overhead-view and one side-view image, achieving R² = 0.79, root mean square error (RMSE) = 95.3, mean absolute error (MAE) = 77.02, and CV = 6.5%. (2) Using MIC algorithm for key feature selection combined with the random forest regression model achieved excellent results (R² = 0.84, RMSE = 81.8, and MAE = 63.3), noticeably outperforming other machine learning models. The deep learning model SeNet (R2 = 0.80, RMSE = 98.1, and MAE = 74.7) outperformed traditional ResNet50 and AlexNet models but showed no significant advantage over the MIC-RFR model. (3) Feature analysis indicated that the projected area and plant height from side-view images, as well as leaf perimeter and green-yellow characteristics from flat-overhead-view images, significantly contributed to leaf area prediction. The contribution of the side-view projected area (+117.4) was substantially greater than that of other features (ranging from 1.48 to 18.87).【Conclusion】 This study employed a simple and efficient leaf area prediction imaging system (one flat-overhead-view combined with one side-view image), integrated with the MIC-RFR model, to meet the high-precision and stable prediction requirements for individual rice leaf area. This method provided a powerful tool and technical support for precision agriculture and crop breeding.

Key words: multi-angle RGB image, morphological feature, color feature, leaf area prediction, machine learning, rice

Fig. 1

Schematic diagram of the study area"

Fig. 2

Technology roadmap"

Table 1

Flat-overhead and side photo features of multi-angle image background culling"

高通量模块化植物表型组平台 High-throughput modular plant phenotyping platform
形态学特征 Morpho feature 颜色特征 Color feature
俯拍图像 FP 侧拍图像 SP 颜色 Color 展示 Show
投影面积 Area 投影面积 Area RGB1
叶周长 Perimeter 叶周长 Perimeter RGB2
紧密度 Compactness 紧密度 Compactness RGB3
植株圆度 Roundness 植株高度 Height RGB4
植株旋转质量对称 RMS 植株宽度 Width RGB5
偏心率 Eccentricity RGB6
叶片细长度 SOL RGB7
各向同性 Isotropy RGB8
RGB9

Fig. 3

Changes in LA of three rice varieties at different growth stages In the figure, RS, TS-E, TS-M, TS-L, PI, FS, MG-M, and MS represent Regreening Stage (14 days after transplanting), Tillering Stage - Early (32 days after transplanting), Tillering Stage - Middle (42 days after transplanting), Tillering Stage - Late (53 days after transplanting), Panicle Initiation (68 days after transplanting), Full Heading Stage (76 days after transplanting), Mid Grain-filling Stage (87 days after transplanting), and Maturity Stage (93 days after transplanting), respectively"

Table 2

LA inversion accuracy results from different shooting angles"

组合 Combination 模型 Model 决定系数 R2 均方根误差 RMSE 平均绝对误差 MAE 变异系数 CV (%)
FP SVR 0.51 147.3 108.1 27.5
RF 0.69 113.9 91.7 9.7
XGBoost 0.68 116.1 92.3 15.8
SP SVR 0.71 107.8 81.4 16.6
RF 0.78 92.5 73.5 10.4
XGBoost 0.73 107.4 81.6 13.7
FP+SP SVR 0.76 104.2 80.7 13.7
RF 0.82 88.2 69.1 5.5
XGBoost 0.76 96.4 73.4 9.2

Fig. 4

LA inversion accuracy at different shooting angles"

Table 3

Evaluation of LA inversion accuracy under different shooting angle combinations"

组合 Combination 决定系数 R2 均方根误差 RMSE 平均绝对误差 MAE 变异系数 CV (%)
FP+1SP（n=40） 0.79 95.30 77.02 6.5
FP+2SP（n=60） 0.80 92.70 72.41 8.4
FP+3SP（n=40） 0.78 95.84 75.86 9.8
FP+4SP（n=10） 0.82 88.20 69.10 5.5

Fig. 5

LA inversion accuracy diagram with different shooting angles The same lowercase letter indicates no significant difference between different image combinations (P>0.05)"

Fig. 6

Feature selection result"

Table 4

Based on the combination of FP+1SP images the accuracy of LA inversion with different feature selections"

特征选择 Feature selection 模型 Model 决定系数 R2 均方根误差 RMSE 平均绝对误差 MAE 变异系数 CV (%)
Full Feature SVR 0.78 97.9 78.4 11.8
RF 0.79 95.4 76.7 4.6
XGBoost 0.78 96.0 76.8 13.5
Pearson SVR 0.77 92.1 71.9 27.5
RF 0.79 102.9 91.7 9.7
XGBoost 0.79 94.2 75.0 8.0
MIC SVR 0.82 85.2 67.1 7.2
RF 0.84 81.8 63.3 7.4
XGBoost 0.80 96.6 73.4 5.8
RFE SVR 0.81 93.3 69.1 5.7
RF 0.82 90.7 72.7 7.1
XGBoost 0.81 92.9 74.8 8.8

Table 5

Based on the combination of FP+1SP images the accuracy of LA inversion with different deep learning models"

模型
Model		 原始 Original 掩膜 Eye masked
决定系数 R2 均方根误差 RMSE 平均绝对误差 MAE 决定系数 R2 均方根误差 RMSE 平均绝对误差 MAE
ResNet50 0.73 105.9 84.47 0.76 103.2 80.78
AlexNet 0.75 105.3 84.66 0.76 102.5 80.46
VGG 0.77 102.3 77.43 0.78 100.4 80.20
SeNet 0.76 103.2 80.83 0.80 98.3 73.73

Fig. 7

VIF values of image features from four angles in the side camera The four oblique angles are 0°, 90°, 180°, and 270°. When VIF≥10, it indicates severe multicollinearity among features"

Fig. 8

Analysis of the importance of key features a and b represent the SHAP analysis results based on Random Forest Regression, while c and d show the correlation between flat-overhead and side projection areas and LA (leaf area)"

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