Hyperspectral image analysis for water stress detection of apple trees

Kim, Yunseop; Glenn, D. Michael; Park, Johnny; Ngugi, Henry K.; Lehman, Brian L.

doi:10.1016/j.compag.2011.04.008

Cited by 95 publications

(48 citation statements)

References 19 publications

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“…This has a powerful capability for use in agricultural crop monitoring, vegetation monitoring, and water quality assessment Kim et al, 2011). In the last decade, HSI technology has been used for detecting bruises and bitter pits on apples and mushrooms (Nicolaï et al, 2006;Gowen et al, 2008), for measuring fruit maturity, firmness and soluble solid content (ElMasry et al, 2007;Lu and Peng, 2007;Noh et al, 2007), for detecting deterioration in mushrooms (Taghizadeh et al, 2010), and for detecting chilling injuries and internal defects in cucumbers (Cheng et al, 2004;Ariana and Lu, 2010).…”

Section: Hsi In Agriculturementioning

confidence: 99%

“…Using a hyperspectral camera, the spectral signature of plant leaves was analyzed to identify the onset and intensity of plant water stress. Kim et al (2011) studied young apple trees inside of a greenhouse with five different levels of water treatment. A hyperspectral camera, along with an active-illuminated spectral vegetation sensor and a digital color camera, was used to monitor the plants within a spectral range of 385-1000 nm.…”

Section: Plant Stress Detectionmentioning

confidence: 99%

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Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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Section: Hsi In Agriculturementioning

confidence: 99%

Section: Plant Stress Detectionmentioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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“…Several researchers argued that remote sensing is a better method to detect and quantify the impact of plant diseases and insect infestations in vegetation compared to visual techniques because a vegetative unit can be repeatedly, objectively and nondestructively examined in a fast, robust, accurate and inexpensive way [54][55][56][57][58]. In addition, it removes human bias in visual interpretation that can be highly variable among individuals [39,59,60].…”

Section: Introductionmentioning

confidence: 99%

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Mırık¹,

Ansley²,

Price³

et al. 2013

ARS

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Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease monitoring and mapping over time and space during the course of a growing season.

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“…There are numerous studies of stress detection in crop leaves based on reflectance data acquired with either single sensor devices or imaging devices, including: biotic stress [1][2][3][4], salinity stress [5], nutrient deficiency [6], and drought stress [3,7]. Carter and Knapp [8] provided a thorough review of reflectance based detection of abiotic and biotic stressors (including dehydration, flooding, freezing, ozone, herbicides, competition, disease, insects, and deficiencies in ectomycorrhizal development and N fertilization) within the 400-850 nm wavelength range when imposed on a wide range of plant species (grasses, conifers, and deciduous trees).…”

Section: Introductionmentioning

confidence: 99%

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

Nansen

2012

Remote Sensing

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Abstract:A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values) were examined as possible indicators of abiotic (irrigation regime) and biotic (spider mite infestation) stressors. Reflectance data was acquired from 2 maize hybrids (Zea mays L.) at multiple time points in 2 data sets (229 hyperspectral images), and data from 160 individual spectral bands in the spectrum from 405 to 907 nm were analyzed. Based on 480 analyses of variance (160 spectral bands × 3 variogram parameters), it was seen that most of the combinations of spectral bands and variogram parameters were unsuitable as stress indicators mainly because of significant difference between the 2 data sets. However, several combinations of spectral bands and variogram parameters (especially nugget values) could be considered unique indicators of either abiotic or biotic stress. Furthermore, nugget values at 683 and 775 nm responded significantly to abiotic stress, and nugget values at 731 nm and range values at 715 nm responded significantly to biotic stress. Based on qualitative characterization of actual hyperspectral images, it was seen that even subtle changes in spatial patterns of reflectance values can elicit several-fold changes in variogram parameters despite non-significant changes in average and median reflectance values and in width of 95% confidence limits. Such scattered stress expression is in accordance with documented within-leaf variation in both mineral content and chlorophyll concentration and therefore supports the need for reflectance-based stress detection at a high spatial resolution (many hyperspectral reflectance profiles acquired from a single leaf) and may be used to explain or characterize within-leaf foraging patterns of herbivorous arthropods.

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Hyperspectral image analysis for water stress detection of apple trees

Cited by 95 publications

References 19 publications

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Remote Monitoring of Wheat Streak Mosaic Progression Using Sub-Pixel Classification of Landsat 5 TM Imagery for Site Specific Disease Management in Winter Wheat

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

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