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作物学报

作物学报 ›› 2025, Vol. 51 ›› Issue (5): 1389-1399.doi: 10.3724/SP.J.1006.2025.43050

• 研究简报 • 上一篇    下一篇

耦合多源无人机遥感数据和机器学习方法的玉米SPAD估测

周科1,2(), 陈鹏飞1,3,*()   

  1. 1中国科学院地理科学与资源研究所 / 资源与环境信息系统国家重点实验室, 北京 100101
    2中国科学院大学, 北京 100049
    3江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023
  • 收稿日期:2024-12-25 接受日期:2025-01-23 出版日期:2025-05-12 网络出版日期:2025-02-11
  • 通讯作者: *陈鹏飞, E-mail: pengfeichen@igsnrr.ac.cn
  • 作者简介:E-mail: zhouke22@mails.ucas.ac.cn
  • 基金资助:
    中国科学院先导A专项(XDA28040502);国家自然科学基金项目(41871344)

Maize SPAD estimation by combining multi-source unmanned aerial vehicle remote sensing data and machine learning methods

ZHOU Ke1,2(), CHEN Peng-Fei1,3,*()   

  1. 1Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences / State Key Laboratory of Resources and Environment Information System, Beijing 100101, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing 210023, Jiangsu, China
  • Received:2024-12-25 Accepted:2025-01-23 Published:2025-05-12 Published online:2025-02-11
  • Contact: *E-mail: pengfeichen@igsnrr.ac.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28040502);National Natural Science Foundation of China(41871344)

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摘要：

为实现玉米精准施肥管理, 准确识别其叶绿素含量具有重要意义。叶片叶绿素相对含量(soil and plant analyzer development, SPAD)值是叶绿素含量的重要指示参数, 已有研究多采用单一数据源, 结合单一机器学习方法来对其反演。为提高SPAD反演精度, 本研究探讨耦合多源无人机遥感影像与多种机器学习方法来开展SPAD值反演的可行性, 并将其与已有方法进行比较。基于不同有机肥、无机肥、秸秆还田以及种植密度处理的玉米田间试验, 获取玉米四叶期和九叶期的无人机多光谱影像和RGB (red-green-blue)影像, 并同步测量了叶片SPAD数据。基于多尺度分析的方法, 将RGB影像与多光谱影像进行融合, 生成既具有高空间分辨率又具有多光谱的融合影像。此外, 基于集成学习思想, 选择BP-人工神经网络法(back propagation-artificial neural network, BP-ANN)、支持向量机法(support vector machine, SVM)、广义加性模型法(generalized additive model, GAM)、随机森林法(random forest, RF)等不同类型机器学习模型, 构建集成学习模型(ensemble learning method, ELM)。基于以上数据源和模型, 设计不同数据源和不同机器学习模型的耦合情景。将数据集分为建模数据集和验证数据集, 基于建模数据集构建每种情景下的SPAD反演模型, 然后基于验证数据集进行模型验证, 对比分析确定最优SPAD反演模型与数据源。相对于单源数据, 多源数据通过融合多光谱影像的光谱信息和RGB影像的纹理信息, 提高了SPAD反演的精度。此外, 相对于单一机器学习方法, 基于集成学习思想耦合多种机器学习方法可以提高SPAD的反演精度。在所有情景中, 基于ELM方法和融合影像的SPAD模型精度最高, 其建模Rcal2为0.83、RMSEcal为1.93, 验证Rval2为0.80、RMSEval为2.07; 其他情景下, 各模型的建模Rcal2在0.64~0.88之间, RMSEcal在1.63~2.84之间; 验证Rval2在0.60~0.78之间, RMSEval在2.18~3.01之间。本研究证明了在反演玉米SPAD时, 最优策略是使用多源数据和集成学习模型, 为进一步的精准氮肥管理提供了技术支撑。

关键词: 机器学习, 多源数据, 玉米, SPAD, 无人机

Abstract:

Accurately identifying chlorophyll content is essential for precise fertilization management in maize. The SPAD (Soil and plant analyzer development) value of leaves serves as a reliable indicator of chlorophyll content. For SPAD prediction using remote sensing, most existing studies rely on single data sources combined with machine learning methods. To enhance SPAD prediction accuracy, this study explores the feasibility of integrating multi-source unmanned aerial vehicle (UAV) data with various machine learning methods, comparing the results to traditional approaches. A maize field experiment was conducted with different treatments, including organic fertilizer, inorganic fertilizer, straw return, and varying planting densities. UAV multispectral and RGB images were acquired at the V4 and V9 growth stages, and SPAD values of maize leaves were measured subsequently. Using a multi-scale analysis approach, RGB images were fused with multispectral images to produce a dataset combining high spatial resolution with multispectral information. Additionally, an ensemble learning method (ELM) was developed by integrating multiple machine learning models, including the backpropagation artificial neural network (BP-ANN), support vector machine (SVM), generalized additive model (GAM), and random forest (RF). Different scenarios were designed by coupling various data sources and machine learning models. The dataset was divided into calibration and validation subsets. SPAD prediction models were developed by calibration dataset, and their performance was evaluated using the validation dataset. Comparative analysis identified the optimal model and data source. Results showed that multi-source data significantly improved SPAD prediction accuracy by combining the spectral information of multispectral images with the texture information of RGB images. Furthermore, the ensemble learning method outperformed single machine learning methods, achieving higher SPAD prediction accuracy. Among all scenarios, the SPAD prediction model using the ELM method and fused images exhibited the highest accuracy, with an a Rcal2 value of 0.83 and RMSEcal value of 1.93 during calibration, and an Rval2 value of 0.80 and RMSEval value of 2.07 during validation. In contrast, models based on other scenarios yielded Rcal2 values ranging from 0.64 to 0.88 and RMSEcal values ranging from 1.63 to 2.84 during calibration, and Rval2 values ranging from 0.60 to 0.78 and RMSEval values ranging from 2.18 to 3.01 during validation. This study demonstrates that the optimal strategy for SPAD prediction in maize involves using multi-source data and ensemble learning models. These findings provide technical support for further advancements in precision nitrogen management.

Key words: machine learning method, multi-source data, maize, SPAD, unmanned aerial vehicle

图1

研究区地理位置及田间试验小区布局 该图基于自然资源部标准地图服务网站GS (2020) 4619号地图绘制, 底图边界无修改。A: 中国; B: 内蒙古自治区; C: 田间试验小区布局。N1: 纯氮用量150 kg hm-2; N2: 纯氮用量180 kg hm-2; N3: 纯氮用量210 kg hm-2; N4: 纯氮用量240 kg hm-2; P1: P2O5用量60 kg hm-2; P2: P2O5用量75 kg hm-2; P3: P2O5用量90 kg hm-2; K1: K2O用量75 kg hm-2; K2: K2O用量90 kg hm-2; K3: K2O用量105 kg hm-2; O1: 有机肥用量0 kg hm-2; O2: 有机肥用量22,500 kg hm-2; O3: 有机肥用量37,500 kg hm-2; O4: 有机肥用量45,000 kg hm-2; O5: 有机肥用量52,500 kg hm-2; D1: 种植密度为50,000 plant hm-2; D2: 种植密度为55,000 plant hm-2; D3: 种植密度为60,000 plant hm-2; D4: 种植密度为62,000 plant hm-2; D5: 种植密度为64,000 plant hm-2; R1: 玉米秸秆还田量0 kg hm-2; R2: 玉米秸秆还田量3000 kg hm-2; R3: 玉米秸秆还田量4500 kg hm-2; R4: 玉米秸秆还田量6000 kg hm-2; R5: 玉米秸秆还田量7500 kg hm-2; T0: 稳定性复合肥用量0 kg hm-2; T1: 稳定性复合肥用量750 kg hm-2。"

表1

本研究中使用的光谱指数"

光谱指数
Spectral indices		 公式
Formula		 数据源
Data source		 发明者
Developer
RVI NIR / R MS image, fused image [17]
EVI 2.5 × (NIR - R) / (NIR + 6 × R - 7.5 × B + 1) MS image, fused image [18]
TVI 0.5 × (120 × (NIR - G) - 200 × (R - G)) MS image, fused image [19]
MSR (NIR / R - 1) / (Sqrt (NIR / R + 1)) MS image, fused image [20]
NDVI (NIR - R) / (NIR + R) MS image, fused image [21]
GNDVI (NIR - G) / (NIR + G) MS image, fused image [22]
NDRE (NIR - RE) / (NIR + RE) MS image, fused image [23]
R_M NIR / RE - 1 MS image, fused image [24]
MNLI 1.5 × (NIR × NIR - R) / (NIR × NIR + R + 0.5) MS image, fused image [25]
VIopt 1.45 × (NIR × NIR + 1) × (R + 0.45) MS image, fused image [26]
VARI (G - R) / (G + R - B) RGB image, MS image, fused image [27]
OSAVI (NIR - R) / (NIR + R + 0.16) MS image, fused image [28]
MCARI ((RE - R) - 0.2 × (RE - G)) × (RE / R) MS image, fused image [29]
MTCI (NIR - RE) / (RE - R) MS image, fused image [30]
EXGR 3 × G - 2.4 × R - B RGB image, MS image, fused image [31]
NGBDI (G - B) / (G + B) RGB image, MS image, fused image [32]
NGRDI (G - R) / (G + R) RGB image, MS image, fused image [33]
IKAW (R - B) / (R + B) RGB image, MS image, fused image [33]
RGBVI (G × G - R × B) / (G × G + R × B) RGB image, MS image, fused image [34]
MGRVI (G × G - R × R) / (G × G + R × R) RGB image, MS image, fused image [34]
CIVE 0.44 × R - 0.88 × G + 0.39 × B + 18.7875 RGB image, MS image, fused image [35]
TCARI/OSAVI 3 × ((RE - R) - 0.2 × (RE - G)× (RE / R)) / OSAVI MS image, fused image [36]

图2

不同情景对比试验技术流程图 BP-ANN: 人工神经网络; SVM: 支持向量机; RF: 随机森林; GAM: 广义加性模型; ELM: 集成学习模型。"

表2

玉米样本SPAD值统计结果"

生育期
Growth stage		 样本数
Number of samples		 最小值
Min.		 最大值
Max.		 平均值
Average value		 标准差
Standard deviation		 方差
Variance
V4 stage 69 39.00 53.10 46.22 2.97 8.84
V9 stage 70 48.20 58.40 54.05 2.11 4.43

图3

影像融合前后对比 缩写同表1和表2。"

图4

不同数据源下SPAD反演效果 A, B: BP-ANN的建模和验证结果; C, D: SVM的建模和验证结果; E, F: GAM的建模和验证结果; G, H: RF的建模和验证结果; I, J: ELM的建模和验证结果。缩写同表1和图2。R2表示决定系数; RMSE表示均方根误差; 下标cal表示建模结果, 下标val表示验证结果。"

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