On developing and enhancing plant-level disease rating systems in real fields

Atoum, Yousef; Afridi, Muhammad Jamal; Liu, Xiaoming; McGrath, J. Mitchell; Hanson, Linda E.

doi:10.1016/j.patcog.2015.11.021

Cited by 35 publications

(17 citation statements)

References 29 publications

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“…These authors developed a computer vision system, CLS Rater, to automatically and accurately rate CLS of whole-plant images in the field according to the 0-10 USDA scale of Ruppel & Gaskill (1971). Experimental results showed CLS Rater to be highly consistent with a rating error of 0.65 compared to 1.31 by human experts (Atoum et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…percentage leaf area affected or rating scales (Vereijssen et al, 2003;Nutter et al, 2006), and by using various technical approaches, i.e. digital image processing (Camargo & Smith, 2009;Patil & Bodhe, 2011;Atoum et al, 2016), analysis of hyperspectral information (Mahlein et al, 2012) or multispectral images (Cui et al, 2010). Different techniques are applied to the source image in different colour spaces to estimate the severity of the disease.…”

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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“…Recent publications (Barbedo, 2014; Pethybridge and Nelson, 2015; Atoum et al, 2016) have explored the use of computer vision to identify diseases in crops at a more complex level. For example, a CNN‐based approach was used to detect and distinguish leaves infected with Cladosporium speckle disease from healthy leaves under challenging photographic conditions, including complex backgrounds and different resolutions, orientations, and illumination levels (Amara et al, 2017).…”

Section: Figurementioning

confidence: 99%

The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

et al. 2020

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The U.S. apple industry, annually worth $15 billion, experiences millions of dollars in annual losses due to various biotic and abiotic stresses, ongoing stress management, and multi-year impacts from the loss of fruit-bearing trees. Over the growing season, apple orchards are under constant threat from a large number of insects, as well as fungal, bacterial, and viral pathogens, particularly in the northeastern United States (Fig. 1). Depending on the incidence and severity of infection by diseases and insects, impacts range from unappealing cosmetic appearance, low marketability, and poor quality of fruit, to decreased yield or complete loss of fruit or trees, causing huge economic losses (Sutton et al., 2014). Early pest and disease detection are critical for appropriate and timely deployment of disease and pest management programs (Bessin et al., 1998). Disease and pest risk prediction models and management programs are developed based on incidence, severity, and timing of infection, taking into account current and forecasted weather data (

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“…The third way is applying polymerase chain reaction (Henson and French, 1993;Schaad et al 2002; Koo et al, 2013) [3][4][5] by biological operation; however, the experimental procedure is complicated for ordinary farmers. With the development of computer vision, another way is image-based recognition of plant disease, which is proposed and applied widely [6][7][8][9][10][11][12][13][14] . Bo Li et al,2015 [15] proposed a shallow artificial neural network model to analyse images of cherry and plum shoots.…”

Section: Introductionmentioning

confidence: 99%

L2MXception: An Improved Xception Network for Classification of Peach diseases 

Yao

Ni²,

Wang

et al. 2020

Preprint

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Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

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On developing and enhancing plant-level disease rating systems in real fields

Cited by 35 publications

References 29 publications

Automated identification of sugar beet diseases using smartphones

Automated identification of sugar beet diseases using smartphones

The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

L2MXception: An Improved Xception Network for Classification of Peach diseases

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On developing and enhancing plant-level disease rating systems in real fields

Cited by 35 publications

References 29 publications

Automated identification of sugar beet diseases using smartphones

Automated identification of sugar beet diseases using smartphones

The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

L2MXception: An Improved Xception Network for Classification of Peach diseases&nbsp;

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L2MXception: An Improved Xception Network for Classification of Peach diseases