Course Content

1. Introduction

The agricultural land mass is more than just being a feeding sourcing in today’s world. Indian economy is highly dependent of agricultural productivity. Therefore in field of agriculture, detection of disease in plants plays an important role. To detect a plant disease in very initial stage, use of automatic disease detection technique is beneficial. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. The affected tree has a stunted growth and dies within 6 years. Its impact is found in Alabama, Georgia parts of Southern US. In such scenarios early detection could have been fruitful.

The existing method for plant disease detection is simply naked eye observation by experts through which identification and detection of plant diseases is done. For doing so, a large team of experts as well as continuous monitoring of plant is required, which costs very high when we do with large farms. At the same time, in some countries, farmers do not have proper facilities or even idea that they can contact to experts. Due to which consulting experts even cost high as well as time consuming too. In such conditions, the suggested technique proves to be beneficial in monitoring large fields of crops. Automatic detection of the diseases by just seeing the symptoms on the plant leaves makes it easier as well as cheaper. This also supports machine vision to provide image based automatic process control, inspection, and robot guidance [2], [4], [5].

Plant disease identification by visual way is more laborious task and at the same time, less accurate and can be done only in limited areas. Whereas if automatic detection technique is used it will take less efforts, less time and become more accurate. In plants, some general diseases seen are brown and yellow spots, early and late scorch, and others are fungal, viral and bacterial diseases. Image processing is used for measuring affected area of disease and to determine the difference in the color of the affected area [1], [2], [6].

Image segmentation is the process of separating or grouping an image into different parts. There are currently many different ways of performing image segmentation, ranging from the simple thresholding method to advanced color image segmentation methods. These parts normally correspond to something that humans can easily separate and view as individual objects. Computers have no means of intelligently recognizing objects, and so many different methods have been developed in order to segment images. The segmentation process is based on various features found in the image. This might be color information, boundaries or segment of an image [11], [13]. We use Genetic algorithm for color image segmentation.

Evolutionary computing was first introduced in the 1960s by I. Rechenberg. His idea was then taken forward by other researchers. Sometimes evolutionary changes are small and appear insignificant at first glance, but they play a part in natural selection and the survival of the species. Examples of natural selections are

1.

The warrior ants in Africa are probably one of the most impressive examples of adaptation. Within any single colony, ants emit a chemical signal that lets the others know they all belong to the same compound. Or, put more simply, a signal that says, “Don’t attack me, we’re all family.” However, warrior ants have learned how to imitate the signal from a different colony. So if a group of warrior ants attacks a colony, they will be able to imitate that colony’s signal. As a result, the workers in the colony will continue on, now under the direction of new masters, without ever realizing an invasion has taken place.

2.

All rat snakes have similar diets, are excellent climbers and kill by constriction. They all have the same reaction when startled (they remain motionless), and will avoid confrontation whenever possible. Some will bite if threatened, although they are non-venomous. However, rat snakes come in a wide variety of colours, from yellow striped to black to orange to greenish. This is because rat snakes are found all over the Eastern and Midwestern states, and are subjected to all types of weather and terrain. Rat snakes are common in urban areas, but they can also be found in wooded areas, mountains or coastal regions. As a result, rat snakes have had to adapt to their local environments in an effort to avoid detection and hunt more effectively.

Genetic algorithms belong to the evolutionary algorithms which generate solutions for optimization problems. Algorithm begins with a set of solutions called population. Solutions from one population are chosen and then used to form a new population. This is done with the anticipation, that the new population will be enhanced than the old one. Solutions which are selected to form new solutions (offspring) are chosen according to their fitness – the more appropriate they are, the more probability they have to reproduce [12], [14].

Some advantages of genetic algorithm are

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Genetic algorithm optimizes both variables efficiently, continuous or discrete.

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It searches from a large sampling of the cost surface.

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Large number of variables can be processed at the same time.

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It can optimize variables with highly complex cost surfaces.

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Gives a number of optimum solutions, not a single solution. So different image segmentation results can be obtained at the same time

The basic steps of genetic algorithm are as follows:

(1)

[Start] Generate random population of n chromosomes (suitable solutions for the problem).

(2)

[Fitness] Evaluate the fitness f(x) of each chromosome x in the population.

(3)

[New population] Create a new population by repeating following steps until the new population is complete.

(a)

[Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected).

(b)

[Crossover] With a crossover probability cross over the parents to form a new offspring (children). If no crossover was performed, offspring is an exact copy of parents.

(c)

[Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).

(d)

[Accepting] Place new offspring in a new population.

 

(4)

[Replace] Use new generated population for a further run of algorithm.

(5)

[Test] If the end condition is satisfied, stop, and return the best solution in current population.

(6)

[Loop] Go to step 2.

2. Literature review

Ghaiwat et al. presents survey on different classification techniques that can be used for plant leaf disease classification. For given test example, k-nearest-neighbor method is seems to be suitable as well as simplest of all algorithms for class prediction. If training data is not linearly separable then it is difficult to determine optimal parameters in SVM, which appears as one of its drawbacks [1].

Authors in paper [2] describe that there are mainly four steps in developed processing scheme, out of which, first one is, for the input RGB image, a color transformation structure is created, because this RGB is used for color generation and transformed or converted image of RGB, that is, HSI is used for color descriptor. In second step, by using threshold value, green pixels are masked and removed. In third, by using pre-computed threshold level, removing of green pixels and masking is done for the useful segments that are extracted first in this step, while image is segmented. And in last or fourth main step the segmentation is done.

Mrunalini et al. [3] presents the technique to classify and identify the different disease through which plants are affected. In Indian Economy a Machine learning based recognition system will prove to be very useful as it saves efforts, money and time too. The approach given in this for feature set extraction is the color co-occurrence method. For automatic detection of diseases in leaves, neural networks are used. The approach proposed can significantly support an accurate detection of leaf, and seems to be important approach, in case of steam, and root diseases, putting fewer efforts in computation.

According to paper [4] disease identification process include some steps out of which four main steps are as follows: first, for the input RGB image, a color transformation structure is taken, and then using a specific threshold value, the green pixels are masked and removed, which is further followed by segmentation process, and for getting useful segments the texture statistics are computed. At last, classifier is used for the features that are extracted to classify the disease. The robustness of the proposed algorithm is proved by using experimental results of about 500 plant leaves in a database.

Kulkarni et al. presents a methodology for early and accurately plant diseases detection, using artificial neural network (ANN) and diverse image processing techniques. As the proposed approach is based on ANN classifier for classification and Gabor filter for feature extraction, it gives better results with a recognition rate of up to 91%. An ANN based classifier classifies different plant diseases and uses the combination of textures, color and features to recognize those diseases [5].

Authors present disease detection in Malus domestica through an effective method like K-mean clustering, texture and color analysis [6]. To classify and recognize different agriculture, it uses the texture and color features those generally appear in normal and affected areas. In coming days, for the purpose of classification K-means clustering, Bayes classifier and principal component classifier can also be used.