Fast and Accurate Detection and Classification of Plant Diseases

Hiary, Hazem; Ahmad, Sahrim; Reyalat, Mohammed; Braik, Malik; Al-Rahamneh, Zainab

doi:10.5120/2183-2754

Cited by 416 publications

(215 citation statements)

References 13 publications

(20 reference statements)

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“…K-Means clustering and neural network have been formulated for clustering and classification of diseases that affect on plant leaf. They found that proposed approach, which can significantly support an accurate detection of leaf diseases in little computational effort [13].…”

Section: Literature Surveymentioning

confidence: 99%

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

Prashar¹,

Talwar²,

Kant³

2015

IJCA

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The cotton leaf disease detection is the process of detecting disease by analyzing their visual properties. The visual properties extraction process from the images is known as the feature extraction. The feature extraction process can be done using the various feature descriptors like SIFT, SURF or other most suitable candidate. The feature descriptors are then passed to the classifier for the evaluation of the feature. The classifier is the algorithm, which is used to classify the feature on the basis of its similarity with the training dataset. The training dataset is the collection of features previously extracted from the known objects (the leaves with specific disease in this case). The leaves with disease are classified on the basis of their similarity with the training dataset of disease samples previously described by the feature descriptors. In this paper, our aim is to solve the cotton disease detection problem using the image processing techniques automatically from the input image. The disease classification will primarily based upon the visibility of the disease on the cotton leaves, which further can be used for the identification using the classifier. The proposed model implementation would be done using the MATLAB simulator and the proposed model results would be obtained in the form of the accuracy, precision, recall, elapsed time and many other similar parameters.

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Section: Literature Surveymentioning

confidence: 99%

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

Prashar¹,

Talwar²,

Kant³

2015

IJCA

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“…Neural Network is popular method in pattern recognition case and its performance in pattern recognition has been proven. Some research about pattern recognition used Neural Network [11][12][13][14][15][16][17][18]. The machine learning process with neural network is depicted in Fig.…”

Section: Neural Networkmentioning

confidence: 99%

Detection of Palm Oil Leaf Disease with Image Processing and Neural Network Classification on Mobile Device

Aji¹,

Munajat²,

Pratama³

et al. 2013

IJCTE

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Abstract-Palm oil is an important agricultural commodity as it has great contribution in producing vegetable oil. Being important commodity, palm oil is threatened by diseases that can attack since early stage. The diseases certainly disrupt the palm tree growth and may decrease the palm oil production. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. This paper propose application of image processing and machine learning in identifying three palm oil diseases based on visual appearances. We design a method with linear complexity process in order to minimize processing time so that it can be implemented in mobile device. Using image processing, 6 types of features are extracted from palm leaf image then the pattern was learned using Neural Network method in machine learning process. The learning process yielded a classification model with 87.75% average accuracy and then a mathematical equation was formulated based on classification model in order to be implemented in mobile device.Index Terms-Image processing, machine learning, mobile device, neural network, palm oil disease.

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“…Generally the disease detection activities are performed by specialists by visually inspecting each sample, which is a very tedious and time consuming task. Hence automatic machine vision technology has acquired a significant role in this field [3].…”

Section: Introductionmentioning

confidence: 99%

Disease Detection in Soya Bean using K-Means Clustering Segmentation Technique

Baghel¹,

Jain²

2016

IJCA

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Automatic detection of leaf disease is an essential research topic in agricultural research. It may prove benefits in monitoring fields and early detection of leaf diseases by the symptoms that appear on the leaves.In this paper K-means clustering method is used for segmentation of diseased portion of soya bean leaf. Percentage calculation and accurate grading of leaf disease is necessary for determination of pest control measures. This approach provides a feasible robust solution for defect segmentation of leaves.

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Fast and Accurate Detection and Classification of Plant Diseases

Cited by 416 publications

References 13 publications

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

Detection of Palm Oil Leaf Disease with Image Processing and Neural Network Classification on Mobile Device

Disease Detection in Soya Bean using K-Means Clustering Segmentation Technique

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