Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy

Jiang, Xiapeng; Zhen, Jianing; Jing, Miao; Zhao, Demei; Wang, Junjie; Jia, Sen

doi:10.1016/j.ecolind.2021.107901

Cited by 36 publications

(13 citation statements)

References 56 publications

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“…In the wake of rapid advancements in computer modeling science, machine learning technology has been applied extensively in crop disease monitoring, with the achievement of remarkable results [ 67 , 68 ]. Jiang et al [ 69 ] demonstrated the high estimation capacity of the RFR model in the monitoring of mangrove disease and insect pests. In addition, Zhang et al [ 70 ] demonstrated the good classification performance of the RFR model in the identification of wheat grains infected with Fusarium .…”

Section: Discussionmentioning

confidence: 99%

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

Feng

Song

Duan

et al. 2021

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

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

Feng

Song

Duan

et al. 2021

Sensors

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“…However, the machine learning algorithms selected for different crops and disease types are different, and the performance of different machine learning methods varies even under the same disease conditions ( Chan et al, 2020 ). Jiang et al (2021) demonstrated the good estimation ability of the RFR model in the study of mangrove diseases, and Zhang et al (2020b) showed the superior classification performance of the RFR model in the identification of wheat grains infected with Fusarium . In this study, three modeling methods were used to establish an estimation model for the severity of wheat powdery mildew disease, and the RFR model performed best; this is mainly because the RFR algorithm has good anti-noise ability, is not easy to fall into over-fitting, and can solve most of the defects in the existing modeling methods ( Meiforth et al, 2020 ).…”

Section: Discussionmentioning

confidence: 98%

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

et al. 2022

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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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“…Nagasubramanian et al [ 11 ] used genetic algorithms as optimizers and support vector machines as classifiers to determine the best band combinations from 240 bands. Jiang, K. et al [ 12 ] used a successive projection algorithm (SPA) to extract sensitive spectral and textural features associated with mangrove pest and disease information and random forest (RF) to model and visualize leaf features at different pest and disease severity levels. Jiang, J. et al [ 13 ], using principal component analysis (PCA) loading coefficients, selected eight feature bands to identify moldy peanuts.…”

Section: Introductionmentioning

confidence: 99%

Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module

Jia

Shi

Luo

et al. 2023

Sensors

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Corn diseases are one of the significant constraints to high–quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and the early symptoms of the two diseases are similar, which can be easily misidentified by the naked eye. In this paper, to address the above problems, a three–dimensional–two–dimensional (3D–2D) hybrid convolutional neural network (CNN) model combining a band selection module is proposed based on hyperspectral image data, which combines band selection, attention mechanism, spatial–spectral feature extraction, and classification into a unified optimization process. The model first inputs hyperspectral images to both the band selection module and the attention mechanism module and then sums the outputs of the two modules as inputs to a 3D–2D hybrid CNN, resulting in a Y–shaped architecture named Y–Net. The results show that the spectral bands selected by the band selection module of Y–Net achieve more reliable classification performance than traditional feature selection methods. Y–Net obtained the best classification accuracy compared to support vector machines, one–dimensional (1D) CNNs, and two–dimensional (2D) CNNs. After the network pruned the trained Y–Net, the model size was reduced to one–third of the original size, and the accuracy rate reached 98.34%. The study results can provide new ideas and references for disease identification of corn and other crops.

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Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy

Cited by 36 publications

References 56 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 Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning

Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module

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