18-01-2013, 01:00 PM
Detection and Classification Of Agricultural Plant Leaf Dieses
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Abstract
Images form important data and information in biological sciences. Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products. Automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and computation of texture statistics for plant leaf diseases. The processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, then the green pixels are masked and removed using specific threshold value, then the image is segmented and the useful segments are extracted, finally the texture statistics is computed. From the texture statistics, the presence of diseases on the plant leaf is evaluated.
Problem definition and Relevance
Plant diseases cause periodic outbreak of diseases which leads to large scale death and famine. Since the effects of plant diseases were devastating, some of the crop cultivation has been abandoned. In India no estimation has been made but it is more than USA because the preventive steps taken to protect our crops are not even one-tenth of that in USA. The naked eye observation of experts is the main approach adopted in practice for detection and identification of plant diseases. But, this requires continuous monitoring of experts which might prohibitively expensive in large farms. Further, in some developing countries, farmers may have to go long distances to contact experts, this makes consulting experts too expensive and time consuming and moreover farmers are unaware of non-native diseases. Automatic detection of plant diseases in an important research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the diseases from the symptoms that appear on the plant leaves. This enables machine vision that is to provide image based automatic inspection, process control and robot guidance. Comparatively, visual identification is labor intensive, less accurate. B. Introduction Plant disease diagnosis is an art as well as science.The diagnostic proces (i.e.recognition of symptomsand signs), is inherently visual and requires intuitive judgment as well as the use of scientific methods.Photographic images of symptoms and signs of plant’s diseases used extensively to enhance description of plant diseases are invaluable in research, teaching and diagnostics etc. Plant pathologists can incorporate these digital images using digital image transfer tools in diagnosis of plant diseases. Farmers are very much concerned about the huge costs involved in these activities. Automatic identification and classification of diseases based on their particular symptoms are very useful to farmers and also agriculture scientists. Early detection of diseases is a major challenge in agriculture science. The development of proper methodology, certainly of use in these areas. Many diseases produce symptoms,which are the main indicators in field diagnosis. As such, several safe practices, the production and processing of plants have been made in the recent past.One of the main concerns of Scientists are the automaticdisease diagnosis and control.
Neural Network Based on Back Propagation Algorithm:
A software routin will be written in MATLAB that would take in .mat files representing the training and test data,train the classifier using the’ train file’ to perform the classification task on the test data. “The Back propagation propagation algorithm is the most important algorithm for the supervised training of multilayer feed-forward ANNs. It derives its name from the fact that error signals are propagated backward through the network on a layer-by-layer basis. We will use a multilayered back propagation neural network (BPNN) as a classifier of different produce and in automatic detection of disease. The number of neurons in the input layer corresponds to the number of input features and the number of neurons in the output layer corresponds to the number of classes.