Image Processing Based Detection of Fungal Diseases in Plants

Pujari, Jagadeesh; Yakkundimath, Rajesh; Byadgi, A. S.

doi:10.1016/j.procs.2015.02.137

Cited by 140 publications

(51 citation statements)

References 15 publications

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“…The results here reported show the potential application of MCFI, in combination with thermography, particularly to classify infiltrated (symptomatic) areas as “healthy” or “infected.” The automatic detection of symptomatic areas has been carried out by other authors using image processing (Al-Hiary et al, 2011; Tian et al, 2012; Pujari et al, 2015). This pre-analysis of images was applied by Huang (2007) to isolate symptomatic areas caused by bacterial diseases in Phalaenopsis seedlings prior to their classification by mathematical models.…”

Section: Discussionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as “healthy” or “infected.” The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

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Section: Discussionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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“…The artificial aperture microwave radar image classification could be a hot topic within the interpretation of SAR pictures 13 . However, the absence of effective feature illustration and also the presence of speckle noise in SAR pictures create classification troublesome to handle.…”

Section: Thementioning

confidence: 99%

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2019

IJPSR

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Agriculture forms a vital part of India"s economy. More than 50% of India"s population is dependent on agriculture for their livelihood. In India, many crops are cultivated, out of which wheat is one of the most important food grains that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India"s economy. Hence, the maintenance of the steady production of the above-stated crop is very important. The main idea of this project is to provide a novel approach based on a deformable model is proposed to handle the segmentation of the system for detecting crop diseases. The existing system is based on the segmentation method simple linear iterative clustering to detect disease in plant leaves. It also shows visual attributes such as color, gradient, texture, and shape to describe the features of leaves. The proposed system will define the cropped image of a wheat plant through image processing and feature extraction algorithms. The RGB color space is converted so that the color information contained in the images can be used effectively to differentiate image and segmentation usage of the k-means algorithm has been suggested.

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“…Regarding the specific use of the ANN technique in the classification of diseases in crops, we find in the literature the works of Kulkarni and Patil [11] (pomegranate), Sannakki et al [5] (grape), and Pujari et al [12] (beans, soybean, sunflower, and tomato). In summary, our results are very competitive compared to those reported in the literature and that only the results reported by Sannakki et al are superior, see Table I.…”

Section: B Obtaining Information By Level Of Damagementioning

confidence: 99%

“…A method for identifying fungal diseases that affect vegetable crops is presented, the approach is based on the ANN technique. ANN 84.11% [12] In this paper, with the intention of promoting the early identification of pests and the levels of damage in tobacco crops, a mobile application it is introduced to achieve the detection of damage in tobacco leaves caused by blue mold. A classification model was trained to boost this application, which is based on the multilayer perceptron ANN technique.…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Neural Networks for Detecting Damage on Tobacco Leaves Caused by Blue Mold

Ávila-George¹,

Valdez-Morones²,

́erez-Espinosa³

et al. 2018

ijacsa

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Abstract-Worldwide, the monitoring of pests and diseases plays a fundamental role in the agricultural sustainability; making necessary the development of new tools for early pest detection. In this sense, we present a software application for detecting damage in tobacco (Nicotiana tabacum L.) leaves caused by the fungus of blue mold (Peronospora tabacina Adam). This software application processes tobacco leaves images using a pattern recognition technique known as Artificial Neural Network. For the training and testing stages, a total of 40 images of tobacco leaves were used. The experimentation carried out shows that the developed model has accuracy higher than 97% and there is no significant difference with a visual analysis carried out by experts in tobacco crop.

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Image Processing Based Detection of Fungal Diseases in Plants

Cited by 140 publications

References 15 publications

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Untitled

Using Artificial Neural Networks for Detecting Damage on Tobacco Leaves Caused by Blue Mold

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