Ground-level hyperspectral imagery for detecting weeds in wheat fields

Herrmann, Ittai; Shapira, Uri; Kinast, Shai; Karnieli, Arnon; Bonfil, David J.

doi:10.1007/s11119-013-9321-x

Cited by 62 publications

(36 citation statements)

References 34 publications

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“…Such existing indices have been used in many applications such as detection of plant stress [49], [51], [52], weed management [6], [7], [13], and diseases identification and detection [53]. Studies of plant stress detection were carried out by Gitelson and Merzlyak [51], and Sims and Gamon [49].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Previous studies on weed management have revealed the importance of using proximal hyperspectral sensors in agricultural applications [6], [7], [13]. Reduction in the workforce needed and the amount of herbicide used and improvement in the production process are notable advantages of weed management.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Activities in HSI have increased recently due to numerous environmental and financial advantages it can bring [6], [7], [13]. For examples, proximal HSI systems (i.e., ground-based) have been used in agriculture to study plant properties and conditions for the purpose of monitoring plant health, detecting diseases, preventing from spreading, and precision control of a herbicide process (i.e., spraying weed species only, hence leading to reduction of herbicide amount, weed species' resistance to herbicide, and pollution effect of herbicide to the environment).…”

Section: Introductionmentioning

confidence: 99%

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Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve

AlSuwaidi

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Recently, there has been a significant increase in the use of proximal or remote hyperspectral imaging systems to study plant properties, types, and conditions. Numerous financial and environmental benefits of using such systems have been the driving force behind this growth. This paper is concerned with the analysis of hyperspectral data for detecting plant diseases and stress conditions and classifying crop types by means of advanced machine learning techniques. Main contribution of the work lies in the use of an innovative classification framework for the analysis, in which adaptive feature selection, novelty detection, and ensemble learning are integrated. Three hyperspectral datasets and a nonimaging hyperspectral dataset were used in the evaluation of the proposed framework. Experimental results show significant improvements achieved by the proposed method compared to the use of empirical spectral indices and existing classification methods.Index Terms-Ensemble learning, feature selection, hyperspectral imaging (HSI), novelty detection (ND), plant monitoring, remote sensing, support vector machine (SVM).

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve

AlSuwaidi

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The process involves a detailed spectral analysis of different kochia plants in different crops (e.g., wheat, barley, and sugar beet), aimed at differentiating between the herbicide-resistant and susceptible biotypes of the weed. Previous work 14 has shown that hyperspectral imagery can be used to distinguish between crop and weeds in field conditions and that glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.)…”

Section: Introductionmentioning

confidence: 99%

Discrimination of herbicide-resistant kochia with hyperspectral imaging

Nugent

Shaw

Jha

et al. 2018

J. Appl. Rem. Sens.

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Abstract. A hyperspectral imager was used to differentiate herbicide-resistant versus herbicidesusceptible biotypes of the agronomic weed kochia, in different crops in the field at the Southern Agricultural Research Center in Huntley, Montana. Controlled greenhouse experiments showed that enough information was captured by the imager to classify plants as either a crop, herbicidesusceptible or herbicide-resistant kochia. The current analysis is developing an algorithm that will work in more uncontrolled outdoor situations. In overcast conditions, the algorithm correctly identified dicamba-resistant kochia, glyphosate-resistant kochia, and glyphosate-and dicamba-susceptible kochia with 67%, 76%, and 80% success rates, respectively. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…While this sort of post classification filtering is a well-accepted method in the field of remote sensing, the application of those in the marine environment is only necessary in this dataset because the spatial resolution is so fine. The images of the marine substrate obtained by the camera were noisier than ones collected on land both because of environmental conditions (e.g., glint) and substrate complexity [58,59]. The spectral similarity of the substrates is greatly exaggerated in these conditions resulting in misclassification (Figure 8).…”

Section: Post-classification Filteringmentioning

confidence: 99%

Ground-Level Classification of a Coral Reef Using a Hyperspectral Camera

Caras

Karnieli

2015

Remote Sensing

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Especially in the remote sensing context, thematic classification is a desired product for coral reef surveys. This study presents a novel statistical-based image classification approach, namely Partial Least Square Discriminant Analysis (PLS-DA), capable of doing so. Three classification models were built and implemented for the images while the fourth was a combination of spectra from all three images together. The classification was optimised by using pre-processing transformations (PPTs) and post-classification low-pass filtering. Despite the fact that the images were acquired under different conditions and quality, the best classification model was achieved by combining spectral training samples from three images (accuracy 0.63 for all classes). PPTs improved the classification accuracy by 5%-15% and post-classification treatments further increased the final accuracy by 10%-20%. The fourth classification model was the most accurate one, suggesting that combining spectra from differ conditions improves thematic classification. Despite some limitations, available aerial sensors already provide an opportunity to implement the described classification and mark the next investigation step. Nonetheless, the findings of this study are relevant both to the field of remote sensing in general and to the niche of coral reef spectroscopy.

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Ground-level hyperspectral imagery for detecting weeds in wheat fields

Cited by 62 publications

References 34 publications

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Discrimination of herbicide-resistant kochia with hyperspectral imaging

Ground-Level Classification of a Coral Reef Using a Hyperspectral Camera

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