Reflectance-based assessment of spider mite “bio-response” to maize leaves and plant potassium content in different irrigation regimes

Nansen, Christian; Sidumo, A.; Martini, Xavier; Stefanova, Katia; Roberts, John D.

doi:10.1016/j.compag.2013.06.007

Cited by 25 publications

(21 citation statements)

References 35 publications

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“…Plants were acclimatized in a glasshouse for 7 days at which point all plants appeared turgid and healthy. A 'push-broom' hyperspectral camera (PIKA II, Resonon Inc., Bozeman, MT, USA) mounted 0.40 m above the canola plants, acquired images of each plant from above, as in Nansen et al (2013b). The technical specifications of the camera were: interface, Firewire (IEEE 1394b); output, digital (12-bit); 240 bands from 392 to 889 nm (spectral resolution = 2.1 nm) (spectral) by 640 pixels (spatial); angular field of view of 7°.…”

Section: Hyperspectral Image Acquisitionmentioning

confidence: 99%

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

et al. 2016

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There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390-890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher & Dustin Severtson classification accuracy (72-100 %) than airborne imagery with 8 mm resolution (69-94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78-88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance.

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Section: Hyperspectral Image Acquisitionmentioning

confidence: 99%

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

et al. 2016

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“…Such studies, showing that established aphid infestations can be detected based on changes in crop reflectance, are promising and important as part of developing sensitive, reliable, and cost-effective crop surveillance systems to improve input management in large-scale agriculture. Similar to Nansen et al (2013a), we argue that it is equally important to develop remote sensing technologies to monitor crop susceptibility, so that "hot spots" with high risk of arthropod infestation can be detected (and necessary responsive actions be implemented) before arthropod-induced crop damage occurs. In other words, it would be preferable to predict crop plant susceptibility based on detectable changes in leaf reflectance, so that crop surveillance can be used in spatiotemporal targeting of insect pest management tactics.…”

Section: Authorsmentioning

confidence: 88%

Increased Susceptibility to Aphids of Flowering Wheat Plants Exposed to Low Temperatures

Lacoste

Nansen

Thompson

et al. 2015

Environmental Entomology

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“…In a recent study of cicada sound production, hyperspectral imaging of forewing costae demonstrated significant differences between mute cicadas and those producing sound by the tymbal (78). Finally, behavioral preference studies with spider mites (Tetranychus cinnabarinus) (98) and aphids (R. padi ) (66) showed how nutrient composition of crop leaves could explain host choices and that reflectance features could be used to predict the choices made by these herbivores.…”

Section: Insect Behaviormentioning

confidence: 98%

“…Numerous methods have been used to classify entomological remote sensing data, including analysis of reflectance values of single spectral bands (93,98), spectral band indices (17,24,79,85,90,95), partial least square (PLS) (2,4,63), principal component analysis (PCA) (90,102), linear discriminant analysis (LDA) (94), decision trees (37), neural network (73), support vector machine (SVM) (111), variogram analysis (90-93, 95, 97, 100), and spatial pattern analysis (5-7). Sometimes reflectance values of spectral bands are transformed prior to classification, and the most common transformations include conversions of reflectance profiles into first-or second-order derivatives (26).…”

Section: Classification Of Remote Sensing Datamentioning

confidence: 99%

Remote Sensing and Reflectance Profiling in Entomology

Nansen

Elliott

2016

Annu. Rev. Entomol.

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Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

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Reflectance-based assessment of spider mite “bio-response” to maize leaves and plant potassium content in different irrigation regimes

Cited by 25 publications

References 35 publications

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

Increased Susceptibility to Aphids of Flowering Wheat Plants Exposed to Low Temperatures

Remote Sensing and Reflectance Profiling in Entomology

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