Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement

Whetton, Rebecca; Waine, Toby; Mouazen, Abdul Mounem

doi:10.1016/j.biosystemseng.2018.01.004

Cited by 48 publications

(45 citation statements)

References 40 publications

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“…The previous studies mainly focused on classification of diseased kernels using hyperspectral imaging or identification of diseased ears under laboratory conditions, and therefore, cannot be applied to FHB identification under field conditions. There is limited literature available in the field environment [15]. The literature that is available is incomplete-it relates only to identification of the disease, and either the ideal identification effect is not achieved or the disease severity is not classified.…”

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confidence: 99%

“…Jin et al (2018) classified wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in wild fields, with the classification accuracy reaching 74.3% [17]. Whetton et al (2018) implemented a hyperspectral line imager for online measurement of FHB wheat in the field, and RGB photos collected from the ground truth plots were used to assess crop disease incidence (the number of individual infected ears in relation to the healthy individuals). The study achieved good accuracy (82%) but did not identify the severity of the disease [15].…”

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confidence: 99%

“…Whetton et al (2018) implemented a hyperspectral line imager for online measurement of FHB wheat in the field, and RGB photos collected from the ground truth plots were used to assess crop disease incidence (the number of individual infected ears in relation to the healthy individuals). The study achieved good accuracy (82%) but did not identify the severity of the disease [15]. This study will make up for the shortcomings of previous studies, using non-imaging hyperspectral techniques to target individual ears to achieve identification and classification of the severity of ear disease.The algorithm of Fisher's linear discriminant analysis (FLDA) performs dimensionality reduction on the original high-dimensional eigenvectors.…”

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confidence: 99%

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Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

Huang

et al. 2019

Applied Sciences

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Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, Fisher's linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB;(2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user's accuracy, and producer's accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%). Appl. Sci. 2019, 9, 3894 2 of 20 development of the FHB disease [3]. In a pandemic year, the ear disease rate is 50% to 100%, which can reduce production by 10% to 40%. In an epidemic year, the diseased rate is 30% to 50%, which can reduce production by 5% to 15% [2]. The main pathogen of wheat FHB is Fusarium graminearum, which accounts for 94.5% of the pathogens in wheat FHB in China. After the infection of wheat, the pathogen can produce a variety of fungal toxins, among which the most toxic one is deoxynivalenol (DON) [2]. DON, commonly known as vomitoxin, has acute adverse effects in animals, including food refusal, diarrhea, emesis, alimentary hemorrhaging, and contact dermatitis [4,5]. FHB of wheat not only causes a significant drop in food production, but the DON produced by pathogens also hurts human and animal health, causing food safety problems [6,7]. Therefore, it is important to monitor the health condition of wheat in the field pre-harvest, and to identify the diseased ears.FHB is caused by fungal infection, which affects the normal physiological function of wheat and changes the external morphology and internal physiologic...

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Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

Huang

et al. 2019

Applied Sciences

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“…Spectral sensors measure the light reflected from the crop canopy [1]. During pathogen attack and disease development on the crop leaf, diseases establish a spectral fingerprint in the reflected leaf signature [8][9][10]. These shifts of the signature can be detected using spectral sensors, particularly in the electromagnetic spectrum from 400-2500 nm [11].…”

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confidence: 99%

“…Depending on the wavelength, these indices can be indicators for crop vitality, general crop stress, pigment content, or a specific plant disease [18,21]. Few works have demonstrated an approach for disease detection using imaging hyperspectral sensors under field conditions [10]. This might be because spectral measurements under field conditions are challenging and the complexity of hyperspectral data is higher than multispectral data [1].…”

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In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

Bohnenkamp

Behmann

Mahlein³

2019

Remote Sensing

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The application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV.

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Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology

Mao,

Li,

et al. 2024

J Sci Food Agric

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BACKGROUNDGray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ‐6) was simulated. Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short‐term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions.RESULTSThe visible red‐light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS‐LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA‐LSTM model excelled, achieving an accuracy of 82.9%.CONCLUSIONThis study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.

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Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement

Cited by 48 publications

References 40 publications

Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale

Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology

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