Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

Goel, Pradeep K.; Prasher, Shiv O.; Patel, R. M.; Landry, Jacques-André; Bonnell, R. B.; Viau, Alain A.

doi:10.1016/s0168-1699(03)00020-6

Cited by 202 publications

(99 citation statements)

References 25 publications

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“…This parameter is influenced by the interaction between the species and the growth stage (Franz et al, 1991;El-Faki et al, 2000;Zhao et al, 2005), by stresses such as water or nitrogen deficiency (Goel et al, 2003;Zhao et al, 2005), by the angle between the leaf and the camera optical axis (Franz et al, 1991;Haralson et al, 1997cited in Noble et al, 2002 and by the substrate nature (Noble and Crowe, 2001;Noble et al, 2002).…”

Section: Fig 1 -Picture Of a Typical Carrot Line (Horizontally In Tmentioning

confidence: 99%

Selection of the most efficient wavelength bands for discriminating weeds from crop

Piron

Leemans

Kleynen

et al. 2008

Computers and Electronics in Agriculture

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The aim of this study was to select the best combination of filters for detecting various weed species located within carrot rows. In-field images were taken under artificial lighting with a multispectral device consisting of a black and white camera coupled with a rotating wheel holding 22 interference filters in the VIS-NIR domain. Measurements were performed over a period of 19 days, starting 1 week after crop emergence (early weeding can increase yields) and seven different weeds species were considered. The selection of the best filter combination was based on a quadratic discriminant analysis. The best combination of filters included three interference filters, respectively centred on 450, 550 and 700 nm. With this combination, the overall classification accuracy (CA) was 72%. When using only two filters, a slight degradation of the CA was noticed. When the classification results were reported on field images, a systematic misclassification of carrot cotyledons appears. Better results were obtained with a more advanced growth stage.

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Section: Fig 1 -Picture Of a Typical Carrot Line (Horizontally In Tmentioning

confidence: 99%

Selection of the most efficient wavelength bands for discriminating weeds from crop

Piron

Leemans

Kleynen

et al. 2008

Computers and Electronics in Agriculture

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“…Standard analytical approaches used in analysis of reflectance data include (see [9] for review): discriminant analysis [10,11,26], principal component analysis [3,21,[26][27][28], multi-regression approaches, like partial least square (PLS) [12,29,30], use of spectral band ratios (indices) [3,[31][32][33], decision trees [34], artificial neural networks [17,28,34], and support vector machines [8,35]. One common denominator in all of these analytical approaches is that they do not incorporate-or take advantage of -the spatial information available in a HI data.…”

Section: Introductionmentioning

confidence: 99%

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

et al. 2010

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Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and -smart applications‖ of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated -rain event‖. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm −1 and 763 cm −1 ). The integrated intensities of these two OPEN ACCESSRemote Sensing 2010, 2 909 peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (-sill‖, -range‖, and -nugget‖) were examined as possible -indicators‖ of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed.

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“…EMLAs include decision tree algorithms and neural networks [5,31], which are computationally fast to implement. CART is a data mining decision-tree that takes spectral and ancillary data and recursively splits it until ending points or terminal nodes are achieved [32,33]. These methods are powerful, and have shown potential for automation.…”

Section: Introductionmentioning

confidence: 99%

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data

Thenkabail

2012

Remote Sensing

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Abstract:The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (80% producer's and user's accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived OPEN ACCESS Remote Sens. 2012, 4 2891 croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (k hat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer's accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (k hat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer's and user's...

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Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

Cited by 202 publications

References 25 publications

Selection of the most efficient wavelength bands for discriminating weeds from crop

Selection of the most efficient wavelength bands for discriminating weeds from crop

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by Combining Landsat, MODIS, and Secondary Data

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