Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching

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

doi:10.1016/j.compag.2014.07.004

Cited by 84 publications

(32 citation statements)

References 18 publications

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“…[Colour figure can be viewed at wileyonlinelibrary.com] 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. Previously these authors used a single-feature two-dimensional xy-colour histogram as input for pixel-wise SVM classification of CLS (Zhou et al, 2014). The additional use of texture features obviously increased the robustness of the detection algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Field monitoring of first disease symptoms is the prerequisite for effective disease control, but is also a labour-intensive, costly, and somewhat subjective activity in large-scale fields (Zhou et al, 2014). Diagnosis of the disease and identification of the causal agent(s) largely depends on expert knowledge and/or long-term experience of the farmer (Nutter et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated identification of sugar beet diseases using smartphones

et al. 2017

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“…Moreover, it has been shown it has highly robust performance in a broad range of applications. The mathematical definition of OCM can be found in our previous work (Zhou et al, 2014).…”

Section: Ocm Based Leaf Trackingmentioning

confidence: 99%

“…In our previous work (Zhou et al, 2014(Zhou et al, , 2013a, we proposed a robust template matching based image algorithm, which achieved continuous small-scale CLS observation under various illumination changes. However, the challenging issue of sandy soil in real field was not addressed.…”

Section: Introductionmentioning

confidence: 99%

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

Rong

Kaneko

Tanaka

et al. 2015

Computers and Electronics in Agriculture

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“…In [5], Pydipati et al quantified diseased tissues in grapefruit leaves (Duncan variety) using texture metrics on the HSI color transform under laboratory conditions. In [6], Zhou et al used a 2D color histogram to train a Support Vector Machine (SVM) classifier and determine if the tissue is damaged or not. In [7], Phadikar et al proposed a Fermi energy-based method for the automatic segmentation of diseased areas in rice leaves.…”

Section: Stemsmentioning

confidence: 99%

An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels

Salazar-Reque¹,

Huaman²,

Kemper³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Quantifying diseased areas in plant leaves is an important procedure in agriculture, as it contributes to crop monitoring and decision-making for crop protection. It is, however, a time-consuming and very subjective manual procedure whose automation is, therefore, highly expected. This work proposes a new method for the automatic segmentation of diseased leaf areas. The method used the Simple Linear Iterative Clustering (SLIC) algorithm to group similar-color pixels together into regions called superpixels. The color features of superpixel clusters were used to train artificial neural networks (ANNs) for the classification of superpixels as healthy or not healthy. These network parameters were heuristically tuned by choosing the network with the best classification performance to obtain the automatic segmentation of the diseased areas. The performance of the classifier was measured by comparing its automatic segmentations with those manually made from a database with public and private images divided into nine groups by visual symptom and plant. The mean error of the area obtained was always below 11%, and the average F-score was 0.67, which is higher than that found by the other two approaches reported in the literature (0.57 and 0.58) and used here for comparison.

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Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching

Cited by 84 publications

References 18 publications

Automated identification of sugar beet diseases using smartphones

Automated identification of sugar beet diseases using smartphones

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

An Algorithm for Plant Disease Visual Symptom Detection in Digital Images Based on Superpixels

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