Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection

Tian, Long; Xue, Bowen; Wang, Ziyi; Liu, Dong; Yao, Xia; Cao, Qiang; Zhu, Yan; Cao, Weixing; Cheng, Tao

doi:10.1016/j.rse.2021.112350

Cited by 85 publications

(49 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The modeling algorithms applied in remote sensing influence the accuracy of remote sensing technologies. Today, the algorithms applied in disease and pest monitoring with remote sensing technologies are mainly empirical models and machine learning algorithms [ 10 , 11 ]. Among them, the empirical models are relatively simple; however, the data are easily influenced by external conditions and have poor universality.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

Feng

Song

Duan

et al. 2021

Sensors

View full text Add to dashboard Cite

Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R2 for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.

show abstract

Section: Introductionmentioning

confidence: 99%

Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

Feng

Song

Duan

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Numerous studies have practiced similar approaches, but most of them fed the pooled dataset to select features instead of considering the severity proportion or period of the problem and were classified between treated and controlled [4,20]. However, very few studies [25,26] explored the consistent behavior of the consistent sensitive features based on severity proportion. We selected the consistent spectral bands (CSBs) to develop indices.…”

Section: Methodology To Feature Selection and Indices Developmentmentioning

confidence: 99%

“…On the contrary, DWT results output coefficients that are not interpretable with the face of original spectra; hence, it causes interpretation difficulty. Many recent plant stress studies [25,26] successfully applied CWT and selected the spectral features on the basis of wavelet scales, which showed very high classification accuracy (CA). Therefore, we imputed CWT in our study for reflectance band selection from non-imaging spectrometer data.…”

Section: Selection Of Consistent Spectral Features By Continuous Wave...mentioning

confidence: 99%

Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with an RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

show abstract

“…Recent studies have shown that feature band selection methods can significantly improve the efficiency of machine learning models without severely sacrificing prediction performance ( Huang et al, 2019a ; Moghaddam et al, 2020 ). However, some studies have found that the performance of machine learning algorithms may be significantly affected by the type of input variables or the number of spectral features ( Hu et al, 2019 ; Maimaitijiang et al, 2019 ), especially in the early detection of rice blast by wavelet analysis combined with the SVM algorithm ( Tian et al, 2021 ). At the same time, Convolutional Neural Networks (CNNs) has been increasingly incorporated in plant phenotyping concepts, successful applied in modeling complicated systems, owing to their ability of distinguishing patterns and extracting regularities from data, such as variety identification in seeds and leaves ( Nasiri et al, 2021 ; Taheri-Garavand et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection

Cited by 85 publications

References 80 publications

Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning

Contact Info

Product

Resources

About