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Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (5): 1389-1399.doi: 10.3724/SP.J.1006.2025.43050

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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 Online:2025-05-12 Published: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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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

Fig. 1

Location of the study area and the layout of field plots This map is based on the map numbered GS (2020) 4619 from Standard Map Service of the Ministry of Natural Resources, with no modifications to the map boundaries. A: China; B: Inner Mongolia Autonomous Region; C: distribution of field plots. N1: pure nitrogen application rate of 150 kg hm-2; N2: pure nitrogen application rate of 180 kg hm-2; N3: pure nitrogen application rate of 210 kg hm-2; N4: pure nitrogen application rate of 240 kg hm-2; P1: P2O5 application rate of 60 kg hm-2; P2: P2O5 application rate of 75 kg hm-2; P3: P2O5 application rate of 90 kg hm-2; K1: K2O application rate of 75 kg hm-2; K2: K2O application rate of 90 kg hm-2; K3: K2O application rate of 105 kg hm-2; O1: organic fertilizer application rate of 0 kg hm-2; O2: organic fertilizer application rate of 22,500 kg hm-2; O3: organic fertilizer application rate of 37,500 kg hm-2; O4: organic fertilizer application rate of 45,000 kg hm-2; O5: organic fertilizer application rate of 52,500 kg hm-2; D1: planting density of 50,000 plant hm-2; D2: planting density of 55,000 plant hm-2; D3: planting density of 60,000 plant hm-2; D4: planting density of 62,000 plant hm-2; D5: planting density of 64,000 plant hm-2; R1: maize straw return amount of 0 kg hm-2; R2: maize straw return amount of 3000 kg hm-2; R3: maize straw return amount of 4500 kg hm-2; R4: maize straw return amount of 6000 kg hm-2; R5: maize straw return amount of 7500 kg hm-2; T0: stabilized compound fertilizer application rate of 0 kg hm-2; T1: stabilized compound fertilizer application rate of 750 kg hm-2."

Table 1

Spectral indices selected in this study"

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]

Fig. 2

Flowchart of comparative experiments under different scenarios BP-ANN: back propagation-artificial neural network; SVM: support vector machine; RF: random forest; GAM: generalized additive model; ELM: ensemble learning method."

Table 2

SPAD statistics in maize"

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

Fig. 3

Comparison of images before and after fusion Abbreviations are the same as those given in Tables 1 and 2."

Fig. 4

SPAD inversion performance under different data sources A, B: calibration and validation results of BP-ANN; C, D: calibration and validation results of SVM; E, F: calibration and validation results of GAM; G, H: calibration and validation results of RF; I, J: calibration and validation results of ELM. Abbreviations are the same as those given in Table 1 and Fig. 2. R2 represents the coefficient of determination; RMSE represents the root mean square error. Subscript cal represents the calibration result, subscript val indicates the validation result."

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