Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis

Shi, Yue; Huang, Wenjiang; Luo, Jiancheng; Huang, Liusheng; Zhou, Xuliang

doi:10.1016/j.compag.2017.07.019

Cited by 76 publications

(41 citation statements)

References 31 publications

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“…This further confirmed that using the joint spatial-spectral information could potentially improve the accuracy of yellow rust detection from high-resolution hyperspectral images [37,38,59]. Previous studies [60,61] showed that the detection accuracy of yellow rust at a leaf scale could reach around 0.88. In general, at a field scale, not all the leaves in infected fields had yellow rust, hence the accuracy of labelling pixels representing healthy leaves in infected fields was limited.…”

Section: Discussionsupporting

confidence: 70%

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

et al. 2019

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Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

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Section: Discussionsupporting

confidence: 70%

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

et al. 2019

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“…For instance, Mahlein, et al [9] analyzed the hyperspectral signatures of cercospora leaf spot, sugar beet rust and powdery mildew on sugar beet plants, and developed specific vegetation indices to detect and identify various diseases with an overall accuracy of 88.3%. Shi, et al [10] tested a total of 18 typical spectral features for the classification of yellow rust, powdery mildew, and aphid on wheat. The results showed the potential of VIs-based kernel discriminant analysis (KDA) for detecting various diseases under complicated farmland circumstances.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat

et al. 2018

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Understanding the progression of host-pathogen interaction through time by hyperspectral features is vital for tracking yellow rust (Puccinia striiformis) development, one of the major diseases of wheat. However, well-designed features are still open issues that impact the performance of relevant models to nondestructively detect pathological progress of wheat rust. The aim of this paper is (1) to propose a novel wavelet-based rust spectral feature set (WRSFs) to uncover wheat rust-related processes; and (2) to evaluate the performance and robustness of the proposed WRSFs and models for retrieving the progression of host-pathogen interaction and tracking rust development. A hyperspectral dataset was collected by analytical spectral devices (ASD) spectroradiometer and Headwall spectrograph, along with corresponding physiological measurements of chlorophyll index (CHL), nitrogen balance index (NBI), anthocyanin index (ANTH), and percentile dry matter (PDM) from the 7th to 41st day after inoculation (dai) under controlled conditions. The resultant findings suggest that the progression of yellow rust on wheat is better characterized by the proposed WRSFs (R 2 > 0.7). The WRSFs-based PLSR model provides insight into specific leaf biophysical variations in the rust pathological progress. To evaluate the efficiency of the proposed WRSFs on yellow rust discrimination during different infestation stages, the identified WRSFs and vegetation indices (VIs) were fed into linear discriminant analysis (LDA) and support vector machine (SVM) classification frames. The WRSFs in conjunction with a SVM classifier can obtain better performance than that of LDA method and the VIs-based models. Overall, synthesizing the biophysical analysis, retrieving accuracy, and classification performance, we recommend the proposed WRSFs for monitoring the progression of the host-pathogen interaction of yellow rust on wheat cross various hyperspectral sensors.

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“…A few successors have launched since, with greater payload capacities and simplicity of use (Yamaha 2014b(Yamaha , 2016. In Japan alone, as of March 2016, about 2,800 unmanned helicopters are registered Huang et al 2014;Yuan et al 2014, Shi et al 2017 Aerial Incl. damage caused by drought or poor fertilization.…”

Section: Actuation Drones For Precision Application Of Pesticidesmentioning

confidence: 99%

Drones: Innovative Technology for Use in Precision Pest Management

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et al. 2019

Journal of Economic Entomology

142

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Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers.

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Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis

Cited by 76 publications

References 31 publications

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat

Drones: Innovative Technology for Use in Precision Pest Management

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