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Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases An automatic approach towards Plant Pathology IEEE PROJECT 2018





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The contribution of a plant is highly important for both human life and environment. Plants do suffer from diseases, like human beings and animals . There is the num ber of plantdiseases that occur and affects the normal growth of a plant. These diseases affect complete plant including leaf, stem, fruit, root, and flower. Most of the time when the disease of a plant has not been taken care of, the plant dies or may cause leaves drop, flowers and fruits drop etc. Appropriate diagnosis of such diseases is required for accurate identification and treatment of plant diseases. Plant pathology is the study of plant diseases, their causes, procedures for controlling and managing them. But, the existing method encompasses human involvement for classification and identification of d iseases. This procedure is time-consuming and costly. Automatic segmentation of diseases from plant leaf images using soft computing approach can be reasonably useful than the existing one. In this paper, we have introduced a method named as Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases automatically. For assigning optimal weight to Radial Basis Function Neural Network (RBFNN) we use Bacterial foraging optimization (BFO) that further increases the speed and accuracy of the network to identify and classify the regions infected of different diseases on the plant leafs. The region growing algorithm increases the efficiency of the network by searching and grouping of seed points having common attributes for feature extraction process. We worked on fungal diseases like common rust , cedar apple rust, late blight, leaf curl, leaf spot, and early blight . The proposed method attains higher accuracy in identification and classification of diseases .




REGION GROWING ALGORITHM (RGA) FOR FEATURE EXTRACTION

RGA is a simple approach that starts with the set of seed points and grows by using these seed points forming a region by appending to each seed the adjoining pixels, having analogous features to the seed such as intensity level, color, or scalar properties for the grayscale images. RGA method delivers the benefit of choosing several measures for selecting a seed point.

There are two basic schemes for this technique termed as 4-neighborhood and 8-neighborhood. The 4-neighborhood leaving diagonally associated regions selects adjacent regions while 8-neighborhood selects both diagonal regions and adjacent regions while growing procedure.


BACTERIAL FORAGING OPTIMIZATION (BFO) FOR TRAINING THE NETWORK

BFO is new nature-inspired optimization algorithms proposed by Kevin Passino in 2002. The group foraging behavior of bacteria such as M. Xanthus and E. Coli. motivated the development of BFO. BFO algorithm is inspired by the chemotaxis behavior of virtual bacteria that move towards (in the direction of) or away (not in the direction of) from the specific signals taking small steps while searching for nutrients in the problem search space is another key concept for BFO.

BFO has turned out to be an effective and influential optimization tool that provides high convergence speed and accuracy applied in the number of the real world applications.


RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN)

RBFNN consists of three layers namely (i) input layer, (ii) hidden layer, and (iii) output layer. The network is are the feed-forward network. The functionalities of the input layer are the same as for other networks i.e. for taking input and providing output, the major difference for any network is lies within the working of hidden layer.

In this network the hidden layer contains the specific activation functions known as Radial Basis Function (RBF). Other than that the hidden layer also comprises of radial kernel functions and output layer comprises of linear neurons.


Existing Sytem

Plants do suffer from diseases, like human beings and animals . There is the num ber of plantdiseases that occur and affects the normal growth of a plant. These diseases affect complete plant including leaf, stem, fruit, root, and flower. Most of the time when the disease of a plant has not been taken care of, the plant dies or may cause leaves drop, flowers and fruits drop etc. Appropriate diagnosis of such diseases is required for accurate identification and treatment of plant diseases.

Plant pathology is the study of plant diseases, their causes, procedures for controlling and managing them. But, the existing method encompasses human involvement for classification and identification of d iseases. This procedure is time-consuming and costly. Automatic segmentation of diseases from plant leaf images using soft computing approach can be reasonably useful than the existing one.


Proposed System

In our proposed work, we focus on identification and classification of plant diseases using some computational intelligence approach. The proposed method uses Radial Basis Function Neural Network (RBFNN) that is trained with the help of Bacterial Foraging Optimization (BFO), to find the affected region via different diseases present on plant leaves. RBFNN is the special linear function having a unique competence of which increases or decreases monotonically with distance from the center point capable of handling the complexity of the affected region exists on the plant leaf images.

The efficiency of the Radial Basis Function Neural Network is further enhanced by using region growing method searching for seed points and grouping them having similar attributes that help in feature extraction process. BFO with its mimicking capability and multi-optimal function verifies to be an efficient and powerful tool for initializing the weight of RBFNN and training the network that can correctly identify different regions on plant leaf image with high convergence speed and accuracy.


Conclusion




The plant serves as the basic need for any living organisms. They are the most important and integral part of our surroundings. Just like a human or other living organism does plant do suffer from different kind of diseases. Such diseases are harmful to plant in a number of ways like can affect the growth of the plant, flowers , fruits, and leaves etc. due to which a plant may even die.

So in this work, we have proposed a novel method named as Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases. The results , when compared with other methods , show that the proposed method achieves higher performance both in terms of identification and classification of plant leaf diseases.


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