Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat

Mırık, Mustafa; Michels, G. J.; Kassymzhanova-Mirik, S.; Elliott, Norman C.

doi:10.1016/j.compag.2007.03.002

Cited by 68 publications

(37 citation statements)

References 52 publications

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“…A number of hyperspectral vegetation indices have been proposed for disease/insect mapping (e.g., Aphid Index, ADHI) or have been proven to be effective in the disease/insect mapping (e.g., DWSI, SIPI, PRI) [8,[14][15][16]. However, few broad-band vegetation indices (VIs) that have potential in reflecting crop stresses were tested for monitoring crop diseases [10,17].…”

Section: Selection Of Spectral Features For Disease Mappingmentioning

confidence: 99%

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

et al. 2014

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Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km 2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods-artificial neural network, mahalanobis distance, and maximum likelihood classifier-were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with OPEN ACCESSRemote Sens. 2014, 6 3612proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.

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Section: Selection Of Spectral Features For Disease Mappingmentioning

confidence: 99%

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

et al. 2014

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“…vegetation biomass and carbon stocks). More specifically, in short stature environments multispectral remote sensing measurements from these passive optical devices are further limited by different factors such as signal scattering from multiple layers of vegetation, bright soil background, relatively open vegetation and spectrally similar & / indiscriminate targets (Jakubauskas et al 2001, Mirik et al 2007, Okin et al 2001, Smith 1997. Further, the sensitivity and accuracy of these systems decline with increasing vegetation biomass and canopy cover (Drake et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Airborne lidar remote sensing applications in non-forested short stature environments: a review

2017

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Abstract. Lidar (light detection and ranging) remote sensing technology provides promising tools for 3D characterization of the earth's surface. In ecosystem studies, lidar derived structural parameters relating to vegetation and terrain have been extensively used in many applications and are rapidly expanding. Yet, most of the lidar applications have focused on tall, woody vegetation in forested environments and less research attention is given to non-forest, short stature vegetation dominated ecosystems. Similar to the lidar developments in forestry, novel methodological approaches and algorithm developments will be necessary to improve estimates of structural and biophysical properties (i.e. biomass and carbon storage) in non-forested short stature environments. Under changing climate scenarios, the latter is particularly useful to improve our understanding of their future role as terrestrial carbon sinks. In an attempt to identify research gaps in airborne lidar remote sensing application in short stature vegetation studies, in this review article we provide a comprehensive overview on the current state of airborne lidar applications. Our focus is mainly on the levels of accuracies and errors reported, as well as the potentials and limitations of the methods applied in these studies. We also provide insights into future research needs and applications in these environments.

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“…Many studies have shown that crop plants under stress have a significant fluctuation in reflectance of the infrared band and the chlorophyll active band, and a consequent shift of the red edge and vegetation indices [10][11][12]. As a harmless, rapid, and costeffective technology, remote sensing has been extensively used by many researchers to study insect-pest-caused stress, such as aphids on wheat [7,[12][13][14][15][16][17][18], rice [11,19,20], cotton [21][22][23], tobacco [24], corn [25], and sorghum [26]. However, an actual rice-paddy system is an extremely complex ecosystem, involving several cross-hazards: the pests themselves, diseases, and nutritional stress simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means

Zhou

Zhang

et al. 2013

Mathematical and Computer Modelling

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a b s t r a c tRice plant-hopper (RPH) (Nilaparvata lugens, Sogatella furcifera, and Laodelphax striatellus) infestation is considered one of the most serious disasters in rice production in Asia. In order to use visible images to detect stress in rice production caused by RPH infestation, an algorithm based on fractal eigenvalues and fuzzy C-means (FCM) has been developed. First, an experiment was designed and many visible images of rice stems were collected. Based on the pretreatment of these visible images, such as smoothing, denoising, image color space and frequency domain transformation, the relative image fractal eigenvalues were extracted by using a box-counting dimension method, and then the linear regression model and FCM clustering algorithm were developed. Results showed that it is possible to distinguish RPH gathering areas and mouldy gray spots after RPH damage on rice plant stems from healthy rice plant stems using fractal-dimension values. Most of the fractal eigenvalues based on different-sized subregional image blocks have higher correlation coefficient with the RPH quantity, and the D skewness (skewness), D mean (mean) and D moment (moment), (where D j is the fractal-dimension value of the j-th subregional image block), based on a 16 × 16 pixel subregional image block, have higher correlation coefficients: 0.803, 0.794, and 0.799 respectively. Using RPH infestation detection and classification based on fractal eigenvalues and the FCM algorithm, the accuracy to differentiate cases in which RPH infestation had occurred or not reached 87%. The accuracy to differentiate four groups was 63.5%. The result is expected to meet the needs of actual rice production: along with the use of a micro-sensor mobile monitoring platform, the visible-imagebased method based on fractal eigenvalues and the FCM algorithm can be used to obtain redundancy information for the large area of remote sensing to the stress induced by RPHs, and then to improve the monitoring accuracy in fusion decisions of integrated pest management.

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Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat

Cited by 68 publications

References 52 publications

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Airborne lidar remote sensing applications in non-forested short stature environments: a review

Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means

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