Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition

Rong, Zhou; Kaneko, Shun’ichi; Tanaka, Fumio; Kayamori, Miyuki; Shimizu, Mitsugu

doi:10.1016/j.compag.2015.05.020

Cited by 39 publications

(9 citation statements)

References 16 publications

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“…Commonly used machine vision recognition mainly depends on visual symptoms. Following segmentation of the leaf from the background, the major features such as color and texture were extracted and used as inputs for machine learning (Zhou et al, 2015; Barbedo et al, 2016). Recently, deep learning methods have been applied to analyze images with leaf lesions.…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Zhou

Jin

Li³

et al. 2019

Front. Plant Sci.

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Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae-infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host–pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.

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Section: Discussionmentioning

confidence: 99%

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Zhou

Jin

Li³

et al. 2019

Front. Plant Sci.

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“…Zhou et al . () proposed a three feature combination L*, a*, entropy × density, for CLS classification and also showed promising results in the field. In combination with single leaf tracking based on robust template matching by orientation code matching, the algorithm was suitable to follow disease development, but without checking the identity of the leaf spots.…”

Section: Discussionmentioning

confidence: 99%

“…Images of disease symptoms on the leaf level avoid many background problems; however, segmentation of regions of interest has to be done and this approach does not allow disease quantification. Zhou et al (2015) proposed a three feature combination L*, a*, entropy 9 density, for CLS classification and also showed promising results in the field. In Figure 5 Schematic representation of data processing for the automated identification of sugar beet leaf diseases based on smartphone images.…”

Section: Discussionmentioning

confidence: 99%

Automated identification of sugar beet diseases using smartphones

et al. 2017

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Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.

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“…An imaging system for continuous plant level (single leaf based) monitoring of Cercospora leaf spot (CLS) in sugar beet by template matching and pattern recognition was presented in [92]. The first stage employs a robust template matching strategy called orientation code matching (OCM) for continuously tracking a single leaf of a plant over time by searching for its corresponding position based on the matching of orientation codes (OC) between two images from adjacent image frames.…”

Section: Visible/rgb Imaging-based Methodsmentioning

confidence: 99%

Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

Kashyap

Kumar

2021

Inventions

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Reducing agricultural losses is an effective way to sustainably increase agricultural output efficiency to meet our present and future needs for food, fiber, fodder, and fuel. Our ever-improving understanding of the ways in which plants respond to stress, biotic and abiotic, has led to the development of innovative sensing technologies for detecting crop stresses/stressors and deploying efficient measures. This article aims to present the current state of the methodologies applied in the field of agriculture towards the detection of biotic stress in crops. Key sensing methodologies for plant pathogen (or phytopathogen), as well as herbivorous insects/pests are presented, where the working principles are described, and key recent works discussed. The detection methods overviewed for phytopathogen-related stress identification include nucleic acid-based methods, immunological methods, imaging-based techniques, spectroscopic methods, phytohormone biosensing methods, monitoring methods for plant volatiles, and active remote sensing technologies. Whereas the pest-related sensing techniques include machine-vision-based methods, pest acoustic-emission sensors, and volatile organic compound-based stress monitoring methods. Additionally, Comparisons have been made between different sensing techniques as well as recently reported works, where the strengths and limitations are identified. Finally, the prospective future directions for monitoring biotic stress in crops are discussed.

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Image-based field monitoring of Cercospora leaf spot in sugar beet by robust template matching and pattern recognition

Cited by 39 publications

References 16 publications

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging

Automated identification of sugar beet diseases using smartphones

Sensing Methodologies in Agriculture for Monitoring Biotic Stress in Plants Due to Pathogens and Pests

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