Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Ren, Yu; Huang, Wenjiang; Ye, Huichun; Zhou, Xuliang; Ma, Huiqin; Dong, Yingying; Shi, Yue; Geng, Yun; Huang, Yanru; Jiao, Quanjun; Xie, Qiaoyun

doi:10.1016/j.jag.2021.102384

Cited by 15 publications

(21 citation statements)

References 37 publications

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“…In this study, diseased leaves were scanned using a LiDE Canon 300 scanner (Canon, Beijing, China), and the DI values were obtained using MATLAB image processing software (version 2016a, Mathworks, Inc., Natick, Massachusetts, USA). The wheat stripe rust DI was expressed by a grading method, and the severity was divided into nine gradients: 0%, 1%, 5%, 10%, 20%, 40%, 60%, 80%, and 100% (percentage refers to the ratio of diseased leaves to the total number of leaves) [1,4,26]. The DI was taken as the closest gradient between the grades.…”

Section: Inoculation and Assessment Of DImentioning

confidence: 99%

“…After the above analysis, a total of 10 vegetation indices, namely, PRI, PSRI, MSR, NDVI, ARI, SIPI, mND705, SRred−edge, CIred−edge, and GI, were selected as the final input features of the model to complete early and mid-term winter wheat stripe rust detection. The physicochemical parameters of the leaf cell internal structure, chlorophyll, anthocyanin, and moisture are all changed after the wheat is infested by stripe rust [4,8,9,26]. The SIPI, PRI, and PSRI were all changed with factors, such as pigment content and photosynthetic rate in green vegetation [8,12].…”

Section: Optimal Feature Selection and Analysismentioning

confidence: 99%

“…The SIPI, PRI, and PSRI were all changed with factors, such as pigment content and photosynthetic rate in green vegetation [8,12]. In addition, the ARI, NDVI, and other vegeta- The physicochemical parameters of the leaf cell internal structure, chlorophyll, anthocyanin, and moisture are all changed after the wheat is infested by stripe rust [4,8,9,26]. The SIPI, PRI, and PSRI were all changed with factors, such as pigment content and photosynthetic rate in green vegetation [8,12].…”

Section: Optimal Feature Selection and Analysismentioning

confidence: 99%

“…In the later stages of spore development, the epidermis will rupture and a rust-colored powdery substance will appear [2]. Therefore, the use of modern information technology to obtain realtime and accurate information on the incidence of wheat stripe rust is important for the accurate prevention and control of wheat diseases, reduction in pesticide pollution, and improvement of wheat yield and quality [4].…”

Section: Introductionmentioning

confidence: 99%

“…The spectral vegetation indices will be changed by wheat stripe rust at different severity levels [4,8,25]. In this study, features were extracted from wheat stripe rust hyperspectral data by CA and CA-RF feature selection algorithms, and the two sets of feature variables were combined with four monitoring algorithms to construct eight early and mid-stripe rust monitoring models.…”

Section: Introductionmentioning

confidence: 99%

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Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

et al. 2022

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Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale.

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Section: Inoculation and Assessment Of DImentioning

confidence: 99%

Section: Optimal Feature Selection and Analysismentioning

confidence: 99%

Section: Optimal Feature Selection and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

et al. 2022

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Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

Xiong,

McCarthy,

Humpal

et al. 2024

Agronomy Journal

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In Australia, the soil‐borne disease common root rot (Bipolaris sorokiniana) (CRR) in wheat (Triticum aestivum L.) leads to substantial yield losses, yet has limited visible aboveground symptoms, making detection and identification labor intensive. Near‐infrared (NIR) spectroscopy offers an early potential identification solution for CRR in wheat and has previously been reported with success for crop disease detection. This study investigated the ability of nondestructive NIR spectroscopy in combination with deep neural networks (DNN), logistic regression (LR), and principal component analysis combined with support vector machines (PCA‐SVM) for early‐stage CRR detection in wheat. NIR spectra of five different wheat varieties with varying resistance to CRR were collected in two seasons of glasshouse and three seasons of field trials using a portable spectrometer. Results demonstrated that DNN outperformed LR and PCA‐SVM, achieving 66%–91% average classification accuracy in glasshouse trials and an average accuracy of 73% with up to 87% in field trials, effectively distinguishing inoculated and non‐inoculated wheat plants from seedling to anthesis stages. Validation with a third season of field data achieved an average of 77% accuracy for the most susceptible variety during the stem elongation stage. NIR reflectance within 1600–1700 nm was identified as most important for estimating CRR presence, with initial detection occurring 35 days after sowing (DAS) in the glasshouse and 46 DAS in the field. In conclusion, a NIR spectrometer with a DNN model successfully performed disease classification, with the potential as a portable early disease detection tool to assist farm management decisions.

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Quantification of rice spikelet rot disease severity at organ scale with proximal imaging spectroscopy

et al. 2023

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Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data

Cited by 15 publications

References 37 publications

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

Near‐infrared spectroscopy and deep neural networks for early common root rot detection in wheat from multi‐season trials

Quantification of rice spikelet rot disease severity at organ scale with proximal imaging spectroscopy

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