基于多角度成像与机器学习的水稻叶面积精确估算

doi:10.3864/j.issn.0578-1752.2025.09.004

Abstract

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

WANG AiDong, LI RuiJie, FENG XiangQian, HONG WeiYuan, LI ZiQiu, ZHANG XiaoGuo, WANG DanYing, CHEN Song. Multi-Angle Imaging and Machine Learning Approaches for Accurate Rice Leaf Area Estimation[J].Scientia Agricultura Sinica, 2025, 58(9): 1719-1734.

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References 62

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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

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

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

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

模型 Model	原始 Original			掩膜 Eye masked
模型 Model	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

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

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