Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.
Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.
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DOI：
10.3389/fpls.2022.828454
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发表时间：
2022
影响因子：
5.6
通讯作者：
Feng W
中科院分区：
文献类型：
--
作者：
Feng ZH;Wang LY;Yang ZQ;Zhang YY;Li X;Song L;He L;Duan JZ;Feng W
小麦白粉病对小麦生长发育有不利影响，并制约产量形成。因此，准确监测该病害对防治白粉病、保障世界粮食安全具有重要意义。利用地物高光谱仪获取小麦开花期和灌浆期的冠层光谱反射率，采用Savitzky-Golay方法对光谱数据进行平滑处理，作为原始反射率。首先，使用均值集中化（MC）、多元散射校正（MSC）和标准正态变量变换（SNV）方法对OR进行谱变换。其次，通过竞争自适应加权采样（汽车）和连续投影算法（SPA）相结合的方法，对上述四种变换后的光谱数据进行特征波段提取。最后，利用偏最小二乘回归（PLSR）、支持向量回归（SVR）和随机森林回归（RFR）构建小麦白粉病病情指数（mDI）的最优监测模型。结果表明，经Pearson相关、双波段优化组合和机器学习方法建模比较，MC谱数据的综合性能最好，是一种较好的疾病谱数据预处理方法。变换后的光谱数据与CARS-SPA算法相结合，能够更有效地提取特征波段。筛选出的条带数多于OR数据提取的条带数，条带位置分布更均匀。在不同机器学习建模方法的比较中，RFR模型表现最好（决定系数，R2 = 0.741-0.852），而SVR和PLSR模型表现相似（R2 = 0.733-0.836）。综合来看，MC方法结合RFR模型（MC-RFR）的光谱数据转换估计精度最高，模型R2为0.849-0.852，均方根误差（RMSE）和平均绝对误差（MAE）分别为2.084 ~ 2.177和1.684 ~ 1.777。与OR结合RFR模型（OR-RFR）相比，R2提高了14.39%，RMSE和MAE的R2分别降低了23.9%和27.87%。开花期的监测精度优于灌浆期，这是由于开花期冠层结构相对稳定的缘故。可以看出，在不改变光谱曲线形状的情况下，利用MC对光谱数据进行预处理，利用汽车和SPA算法提取特征波段，并利用RFR建模方法增强多变量之间的协同作用，建立了模型（MC-CARS-SPA-RFR）能更好地提取冠层光谱与病害之间的协变关系，从而提高小麦白粉病的监测精度。该研究成果为实现农作物病害状况的高精度遥感监测提供了思路和方法。
Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky–Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS–SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R2 = 0.741–0.852), while the SVR and PLSR models performed similarly (R2 = 0.733–0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R2 was 0.849–0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R2 increased by 14.39%, and the R2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.
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