Classification of Rubber Tree Leaf Diseases Using Multilayer Perceptron Neural Network

Abdullah, Noor Ezan; Rahim, A’zraa Afhzan Ab; Hashim, Hadzli; Kamal, Mahanijah Md

doi:10.1109/scored.2007.4451369

Cited by 45 publications

(26 citation statements)

References 5 publications

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“…Abduhlah et al [15] classified three pathogens for rubber trees achieving 80% of correct guesses. However, Abdulah and co-workers' tests considered only one kind of pathogens.…”

Section: Comparison To Related Workmentioning

confidence: 99%

Identification of Foliar Diseases in Cotton Crop

Bernardes

Rogéri

Oliveira

et al. 2013

Lecture Notes in Computational Vision and Biomechanics

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The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. Proposed MethodThe classification process was divided into two phases:  Phase 1: Finding the best feature vector for each class;  Phase 2: Create the final classification system from the best results obtained in the previous phase. Phase 1: Finding the Best Feature VectorThis phase is aimed at finding the best feature vector to represent each of the classes to be considered during classification. To achieve this goal the following steps were accomplished: Decomposition of images into multiple channels (R, G, B, H, S, V, I3a, I3b, and GL);  Application of the discrete wavelet transform (DWT) up to the third level;  Computation of the energy for each sub-band and compose the feature vector; Creation of the SVM classification environment;  Listing of the images used for training and testing;  Evaluation of the best feature vectors. Decomposition of the ImageThe decomposition of the images is the first process the system executes. In this stage an image is decomposed into nine channels, namely: R, G, B, H, S, V, I3a, I3b and GL. Application of the Discrete Wavelet TransformDiscrete Wavelet Transform (TWD) decomposition is applied up to the third level. When an image is decomposed as such it will have ten sub-bands, as illustrated in Figure 3. Note that each sub-band is identified by a number between 1 and 10. Region A1 and sub-bands 8, 9 and 10, are generated by the first level of decomposition of the DWT. Region A2 and sub-bands 5, 6 and 7 refer to the second level of decomposition, and the third level is formed by the sub-bands 1, 2, 3 and 4. Computation of the Energy for Each Sub-bandAfter applying the DWT to the three levels, the energy for each wavelet sub-band is computed. Each value obtained is inserted into a feature vector as the one illustrated in Figure 4. The vector in this figure consists of ten elements, each of them is identified by a number corresponding to the number of the sub-band in Figure 3. The energy value computed for each sub-band is stored in the corresponding vector element.Vector Features 1 2 3 4 5 6 7 8 9 10 Figure 4: Example of the structure of a feature vector. Creation of an SVM Classification EnvironmentThe network architecture used is shown in Figure 5. Note that 10 input elements are used. To each input element is assigned the value of the element of the corresponding characteristic vector. In the hidden layer there are a number of neurons (N) equal to the number of training examples, making the net converge...

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“…Abduhlah et al [15] classified three pathogens for rubber trees achieving 80% of correct guesses. However, Abdulah and co-workers' tests considered only one kind of pathogens.…”

Section: Comparison To Related Workmentioning

confidence: 99%

Identification of Foliar Diseases in Cotton Crop

Bernardes

Rogéri

Oliveira

et al. 2013

Lecture Notes in Computational Vision and Biomechanics

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“…Like the quasi-Newton methods, the LevenbergMarquardt algorithm was designed to approach secondorder training speed without having to compute the Hessian matrix [6].When the performance function has the form of a sum of squares (as is typical in training feed forward networks), then the Hessian matrix can be approximated as,…”

Section: A Lavenberg Marquardt Algorithmmentioning

confidence: 99%

Comparison between various supervised ANN algorithm using RGB indices for plaque lesion classification

Abdullah

Hashim

Osman

et al. 2010

2010 International Conference on Electronic Devices, Systems and Applications

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1 ,hadzli120@salam.uitm.edu.my 2 , nazmie052@salam.uitm.edu.my 3Abstract-Color and color difference are important information for a lesion in dermatological diagnosis. This paper presents various supervised ANN models for plaque classification using RGB indices. Images are taken from selected skin at the dermatological clinic which the images are captured using digital camera with controlled environment. The analysis of dermatological digital images is performed by measurements of the pixels that represent a segmented cropped area of the skin lesion. The segmentation of the skin image could be accomplished automatically by supervised segmentation algorithms. The proposed models are designed by implementing Levenberg Marquardt feedforward, Radial Basis Function algorithm and backpropagation. Each optimized model are evaluated and validated through analysis of the performance indicators regularly applied in classification models. At later stage, the best performance from these models is compared with other researchers' work in this area, but using different color space such as YCbCr and HSV.

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“…pumpkin, pepper, bean) [8] using image color analysis, which tried to discriminate the diseases based on color difference. The method in [9] tried to discriminate a given disease from other pathologies of rubber tree leaves based on PCA and Neural networks, which directly applied to the RGB values of the pixels of a low resolution (15 × 15 pixels) and does not employ any image segmentation techniques. The system [10] was proposed to monitor the health vineyard, which mainly used thresholding to separate diseased leaves and ground from healthy leaves using both RGB and HSV color representation of the image and morphological operations.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Severity Assessment of Crop Diseases Using Image Pattern Recognition

Han

Haleem

Taylor

2016

Studies in Computational Intelligence

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Disease diagnosis and severity assessment are necessary and critical for predicting the likely crop yield losses, evaluating the economic impact of the disease, and determining whether preventive treatments are worthwhile or particular control strategies could be taken. In this work, we propose to make advances in the field of automatic detection and diagnosis and severity assessment of crop diseases using image pattern recognition. We have developed a two-stage crop disease pattern recognition system which can automatically identify crop diseases and assess sevrity based on combination of marker-controlled watershed segmentation, superpixel based feature analysis and classification. We have conducted experimental evaluation using different feature selection and classification methods. The experimental result shows that the proposed approach can accurately detect crop diseases (i.e. Septoria and Yellow rust, which are the two most important and major types of wheat diseases in UK and across the world) and assess the disease severity with efficient processing speed.

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Classification of Rubber Tree Leaf Diseases Using Multilayer Perceptron Neural Network

Cited by 45 publications

References 5 publications

Identification of Foliar Diseases in Cotton Crop

Identification of Foliar Diseases in Cotton Crop

Comparison between various supervised ANN algorithm using RGB indices for plaque lesion classification

Automatic Detection and Severity Assessment of Crop Diseases Using Image Pattern Recognition

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