SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

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

doi:10.9781/ijimai.2016.371

Cited by 79 publications

(47 citation statements)

References 10 publications

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“…Focusing on the convolution layer, the normalised Kernel was used to convolve the input images of both diseased and healthy maize leaves. The convolution operation is explained in Equation (9).…”

Section: Methodsmentioning

confidence: 99%

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A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

2019

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Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognised and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves. The northern corn leaf blight (Exserohilum), common rust (Puccinia sorghi) and gray leaf spot (Cercospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.

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Section: Methodsmentioning

confidence: 99%

“…A study in comparison of support vector machine (SVM) and ANN, was performed by Pujai et al [9]. Algorithms for colour extraction and texture features were developed, which were thus used to train SVM and ANN classifiers.…”

Section: Related Workmentioning

confidence: 99%

A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

2019

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“…SVM being the optimal binary classifier suits well to the problem in hand. SVM is a supervised model for classification which is widely used in Machine Learning and Pattern Recognition [38], [39], [40]. The model is first trained by providing both positive and negative training examples.…”

Section: B Support Vector Machine (Svm)mentioning

confidence: 99%

Driver Fatigue Detection using Mean Intensity, SVM, and SIFT

Naz¹,

Ziauddin²,

Raza³

2019

IJIMAI

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Driver fatigue is one of the major causes of accidents. This has increased the need for driver fatigue detection mechanism in the vehicles to reduce human and vehicle loss during accidents. In the proposed scheme, we capture videos from a camera mounted inside the vehicle. From the captured video, we localize the eyes using Viola-Jones algorithm. Once the eyes have been localized, they are classified as open or closed using three different techniques namely mean intensity, SVM, and SIFT. If eyes are found closed for a considerable amount of time, it indicates fatigue and consequently an alarm is generated to alert the driver. Our experiments show that SIFT outperforms both mean intensity and SVM, achieving an average accuracy of 97.45% on a dataset of five videos, each having a length of two minutes.

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“…Savita N. Ghaiwat, Parul Arora developed to extract some features of color and texture and which were used to train SVM and ANN classifiers. The study proved that SVM method has presented the best performance in terms of accuracy estimated (92.17%)and this ratio is better than the ANN classifier where accuracy value estimated (87.48%) [4]. Jasmeet used BP, PCA combined with SVM to increase the detection accuracy of the diseased plant leaves.…”

Section: Literature Reviewmentioning

confidence: 99%