Airborne Multi-Spectral Remote Sensing of Russian Wheat Aphid<sup>1</sup>Injury to Wheat

Elliott, Norman C.; Mırık, Mustafa; Yang, Zhiming; Dvorak, Tom; Rao, Mahesh; Michels, Jerry; Walker, T. J.; Catana, Vasile; Phoofolo, Mpho W.; Giles, K. L.; Royer, T. A.

doi:10.3958/0147-1724-32.4.213

Cited by 28 publications

(12 citation statements)

References 6 publications

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“…Of this damage, almost 60% occurred in the Texas and Oklahoma Panhandles, northeastern Colorado, western Kansas, and southwestern Nebraska (Smith et al 2004). Infestations by Russian wheat aphid are unpredictable in time and space (Elliott et al 2005), and when aphids are extremely abundant, yield can be reduced 100% (Elliott et al 2007). Thus, during favorable growing conditions, Russian wheat aphid can greatly damage wheat and barley in a short period of time.…”

Section: Introductionmentioning

confidence: 97%

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Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid¹in Winter Wheat

Mırık¹,

Ansley²,

Michels³

et al. 2009

Southwestern Entomologist

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Section: Introductionmentioning

confidence: 97%

“…Percentage Reduction in Yield and Biomass of Winter Wheat Noninfested and Infested by Russian Wheat Aphid in 10 Fields inTexas and Oklahoma in 2004, 2005, and 2006 Grain yield (mean g/m 2 ) Fields 1-6 = dryland winter wheat (Field 1 in Deaf Smith County, TX, and Fields 2-6 in Cimarron County, OK); Fields 7-10 = irrigated winter wheat in Cimarron County, OK.…”

mentioning

confidence: 99%

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid¹in Winter Wheat

Mırık¹,

Ansley²,

Michels³

et al. 2009

Southwestern Entomologist

Self Cite

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“…The reflectance from a rice canopy at the red (670-690 nm) and nearinfrared (760-1 100 nm) wavebands is significantly related to nitrogen fertilizer rates at the tillering stage (Nguyen and Lee 2006). Meanwhile, spectral reflectance from crops could also relate to the damage severity caused by insect pests (Elliott et al 2009;Fraulo et al 2009;Abdel-Rahman et al 2010), because the pest infestation causes large changes in chlorophyll, water content and leaf structure of crop (Prabhakar et al 2013). Rice damaged by BPH could be detected using the reflectance spectra; reflectance in the region of 737-890 nm significantly decreased as damage severity increased (Sun and Liu 2010).…”

Section: Introductionmentioning

confidence: 99%

Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer

et al. 2014

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Reflectance and SPAD readings were measured in rice grown under different nitrogen fertilizer rates and infested with different numbers of brown planthopper (BPH), Nilaparvata lugens, to assess the relationships among these variables. The results showed that SPAD readings and reflectance from rice were significantly affected by BPH infestations and nitrogen fertilizer rates, whereas there was no interaction between the two factors. SPAD readings and reflectance decreased as BPH infestations increased but they increased as nitrogen fertilizer rates were increased. SPAD readings varied with position of leaf on the same stem of rice. The fourth and fifth leaves were more sensitive to BPH infestations than the first and second regardless of application rates of urea. The ratio indices of SPAD readings of the fourth to first leaf (RSPAD 4/1 ), and fourth to second leaf (RSPAD 4/2 ) were significantly related to BPH infestations, and they were relatively independent of nitrogen fertilizer rates in the single stem rice. The spectral reflectance from rice canopy significantly decreased in the near-infrared wavelength range as BPH infestations increased. The modified chlorophyll absorption ratio index (MCARI 710 ) was found more suitable to relate the numbers of BPH under different nitrogen fertilizer rates and durations of BPH infestation. The main effects of BPH infestations on SPAD reading and reflectance indices were consistent regardless of nitrogen application rates. Therefore, SPAD reading and spectral indices have potential to detect BPH infestations in rice fields.

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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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Airborne Multi-Spectral Remote Sensing of Russian Wheat Aphid¹Injury to Wheat

Cited by 28 publications

References 6 publications

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid¹in Winter Wheat

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid¹in Winter Wheat

Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer

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

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Airborne Multi-Spectral Remote Sensing of Russian Wheat Aphid1Injury to Wheat

Cited by 28 publications

References 6 publications

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid1in Winter Wheat

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid1in Winter Wheat

Detection of brown planthopper infestation based on SPAD and spectral data from rice under different rates of nitrogen fertilizer

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

Contact Info

Product

Resources

About

Airborne Multi-Spectral Remote Sensing of Russian Wheat Aphid¹Injury to Wheat

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid¹in Winter Wheat

Grain and Vegetative Biomass Reduction by the Russian Wheat Aphid¹in Winter Wheat