Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Carroll, Matthew; Glaser, John A.; Hellmich, Richard L.; Hunt, Thomas E.; Sappington, Thomas W.; Calvin, Dennis D.; Copenhaver, Ken; Fridgen, John

doi:10.1093/jee/101.5.1614

Cited by 29 publications

(13 citation statements)

References 12 publications

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“…Other researchers successfully used spectral data to detect Magnaporthe grisea on rice (Kobayashi et al 2001), Phytophthora infestans on tomato (Zhang et al 2003), and Venturia inaequalis on apple trees (Delalieux et al 2007). Damage to crops caused by virus diseases (Naidu et al 2009) or insects (Carrol et al 2008) could also be detected using spectral sensors.…”

Section: Spectral Signature Of Plant Diseasesmentioning

confidence: 99%

Recent advances in sensing plant diseases for precision crop protection

et al. 2012

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Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach-including plant pathology, engineering, and informatics-is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.

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Section: Spectral Signature Of Plant Diseasesmentioning

confidence: 99%

Recent advances in sensing plant diseases for precision crop protection

et al. 2012

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“…Wavelengths and indices sensitive to chlorophyll a and b and the byproducts of chlorophyll degradation (anthocyanins and carotenoids) have been explored in various plants (Chappelle et al 1992, Datt 1998, Blackburn 2007, Carroll et al 2008. Indices sensitive to anthocyanins and carotenoids often focus on wavelengths in the green, red, and near-infrared portions of the spectrum Merzlyak 2004, Gitelson et al 2009).…”

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confidence: 99%

“…Moreover, the bounds of these indices are most likely species dependent, unlike an index such as the normalized difference vegetative index (Rouse et al 1974), which ranges from Ϫ1 to 1. Carroll et al (2008) suggested that the amalgamation of such indices combined with agronomic information might be used to distinguish insect stress from other types of stressors. Some of the NIR wavelengths are sensitive to multiple factors involving crop health.…”

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Remotely Sensing Arthropod and Nutrient Stressed Plants: A Case Study With Nitrogen and Cotton Aphid (Hemiptera: Aphididae)

Reisig¹,

Godfrey

2010

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Remote sensing can be used in combination with ground sampling to detect aphid- (Aphis gossypii Glover) infested cotton (Gossypium hirsutum L.). Changes in wavelengths in the near-infrared (NIR) have proven useful for such detection, but these changes can be confused with other factors stressing plants, such as water deficiency and nutrient status. This study was designed to test the utility of this technology to distinguish between two factors stressing plants: nitrogen deficiency and aphids. Field plots were created by applying varying rates of nitrogen to cotton at different dates in the growing season in 2003 and 2004. Subplots were created by applying disruptive insecticides, which increased aphid populations in a portion of the subplots. Airplane and satellite remote sensing data in 2003 and 2004 were supplemented with ground sampling of aphid populations in both years. Insecticide application, nitrogen application rate and date influenced aphid abundance. Cotton with higher aphid populations could be distinguished from cotton with natural aphid infestations independent of plant nitrogen status using a NIR wavelength in 2003 and a proprietary 2004 index. Complex distinctions among varying nitrogen treatments and aphid abundance were not possible using this data. In the future, possible confounding factors should be investigated from the perspective of their change on crop physiology before remote sensing can be used in an integrated pest management (IPM) program.

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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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Use of Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery for Detection of European Corn Borer Infestation in Iowa Corn Plots

Cited by 29 publications

References 12 publications

Recent advances in sensing plant diseases for precision crop protection

Recent advances in sensing plant diseases for precision crop protection

Remotely Sensing Arthropod and Nutrient Stressed Plants: A Case Study With Nitrogen and Cotton Aphid (Hemiptera: Aphididae)

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

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