Rice disease identification using pattern recognition techniques

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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“…Phadicar and Sil [17] studied rice crops achieving 92% of correct diseases classification. However, only two classes were used for this study.…”

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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“…[ Phadikar and Sil 2008] [23] designed a method to detect and classify two rice crop diseases i.e. rice blast and brown spot.…”

Section: Plant Recognition and Classification Techniquesmentioning

confidence: 99%

Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Sabrol¹,

Kumar²

2015

IJCA

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The plant diseases are a normal part of nature but can cause significant economic, social and ecologic loss globally. It's difficult to monitor continuously plant health and detection of diseases. The paper presents a survey of recent studies on the area of plant disease recognition and classification from digital images using image processing and soft computing techniques. The main aim of the paper is to focus on the area of plant pathology recognition and classification only. The paper is omitting the disease severity quantification. Although the paper, considering the images of symptoms presents on plant leaves and stems only for limiting the survey. Each considered paper in the review, representing the comprehensive details of the technical implementation of an algorithm. The algorithm begins with digital image acquisition of infected and non-infected plants; perform image preprocessing, differentiate disease infected region from a non-infected region using segmentation, extract features from segmented images for recognition and classification. This survey expected to useful for researchers from plant pathology and pattern recognition field.

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“…[11]Image segmentation techniques to identify infected part of the plants. [12] The lesion areas with anthracnose and frog-eye spots on a leaf of tobacco seedlings are segmented by contrast stretching transformation with an adjustable parameter and morphological operations. [13] In this work novel approach combines low-cost NIR filters used on a standard camera with neural networks to achieve a significantly higher accuracy as compared to classic threshold techniques.…”

Section: Literature Reviewand Related Studiesmentioning

confidence: 99%

Homogenous Segmentation based Edge Detection Techniques for Proficient Identification of the Cotton Leaf Spot Diseases

Revathi¹,

Hemalatha²

2012

IJCA

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In this work we express technological strategies using mobile captured symptoms of cotton leaf spot images and classify the diseases using neural network. The system has been trained to achieve intelligent farming for rural area farmers, including early recognition of diseases in grows, selective fungicide application,etc..This research work proposes an automatic image preprocessing techniques. At first, the captured images are processed for improvement. Other edge detectors presented in earlier works can detect edges on different size objects. In this Research work, a homogeneity operator can take the difference of the center pixel and a pixel that is two or three pixels away. The major objective of this Research work is to use Homogeneity-based edge detector segmentation, which takes the result of any edge detector and divides it by the average value of the area. This work has been implemented in the real time software and produces best results.

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Rice disease identification using pattern recognition techniques

Cited by 216 publications

References 5 publications

Identification of Foliar Diseases in Cotton Crop

Identification of Foliar Diseases in Cotton Crop

Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Homogenous Segmentation based Edge Detection Techniques for Proficient Identification of the Cotton Leaf Spot Diseases

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