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In this paper, we have studied the two classification techniques for paddy plant disease. Then we presented a new technique for classification of diseased paddy plant using global contrast enhancement. The work is developed using MATLAB and tested on different images. The results shows very promising as the segmentation are very correct. Keywords—Digital image processing, image equalization A Detailed review and Classification of Segmented Image for Paddy Plant Disease 420 .Therefore; looking for fast, automatic, less expensive and accurate method to detect plant disease cases is of great realistic significance Agriculture has become much more than simply a means to feed ever growing populations. Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. There are several diseases that affect plants with the potential to cause devastating economical, social and ecological losses. In this context, diagnosing diseases in an accurate and timely way is of the utmost importance. There are several ways to detect plant pathologies. Some diseases do not have any visible symptoms associated, or those appear only when it is too late to act. In those cases, normally some kind of sophisticated analysis, usually by means of powerful microscopes, is necessary. In other cases, the signs can only be detected in parts of the electromagnetic spectrum that are not visible to humans. A common approach in this case is the use of remote sensing techniques that explore multi and hyperspectral image captures. The methods that adopt this approach often employ digital image processing tools to achieve their goals. Detection Because the information gathered by applying image processing techniques often allows not only detecting the disease, but also estimating its severity, there are not many methods focused only in the detection problem. There are two main situations in which simple detection applies: • Partial classification: when a disease has to be identified amidst several possible pathologies, it may be convenient to perform a partial classification, in which candidate regions are classified as being the result of the disease of interest or not, instead of applying a complete classification into any of the possible diseases. Timely diagnosis of crop diseases in fields is critical for precision on-farm disease management. Remote sensing technology can be used as an effective and inexpensive method to identify diseased plants in a field scale. However, due to the diversity of crops and their associated diseases, application of the technology to agriculture is still in research stage, which needs to be elaborately investigated for algorithm development and standard image processing procedures. In this paper, we examined the applicability of broadband high spatial-resolution ADAR (Airborne Data Acquisition and Registration) remote sensing data to detect rice sheath blight and developed an approach to further explore the applicability. Based on the field symptom measurements, a comprehensive field disease index (DI) was constructed to measure infection severity of the disease and to relate to image sampled infections. In addition to direct band digital number (DN) values, band ratio indices and standard difference indices were used to examine possible correlations between field and image data. The results indicated that the broadband remote sensing imagery has the capability to detect the disease. Some image indices such as RI14, SDI14 and SDI24 worked better than others. A correlation coefficient above 0.62 indicated that these indices would be valuable to use for identification of the rice disease. In the validation analysis, we obtained a small root mean square error (RMS = 9.1), confirming the applicability of the developed method. Although the results were encouraging, it was difficult to discriminate healthy plants from light infection ones when DI < 20 because of their spectral similarities. Hence, it was clear that identification accuracy increases when infection reaches medium-to-severe levels (DI > 35). This phenomenon illustrated that remote sensing images with higher spectral resolution (more bands and narrower bandwidth) were required in order to further examine the capability of separating the light diseased plants from healthy plants. II. SEGMENTATION USING NORMALIZED CUT In this work a paddy plant disease image segmentation method is presented which uses gap portioning method for classification. Since it only uses the gap portioning for the segmentation it misses many colours which are out of the limits of the thresholds set. Since the angle of image taken varies it is very difficult to manage colours with this segmentation method. This study also looses the emasculate results because the prime focus is on cost reduction which compromises the overall quality of segmentation. We can use the image equalization method which will enhance the segmentation results of the system. We will be eradicating the problem of colour variation due to angle by boosting the colours using image equalization. The use of image equalization can automatically reduce the error in segmentation which can improve segmentation results unlike the research in which they, for reducing the cost of the system have compromised with the results. Er. Jasdeep Kaur and Dr. Sanjay Singla 421  III. TEXTURE ANALYSIS METHOD In this it is presented that the segmentation can be done very efficiently using the global and automatic thresholding methods which increase the step complexity of the proposed system. Since the light variations are uncontrollable in these kinds of images the use of automatic thresholding increases the vulnerability of the system. Due to the lack of control over the lighting conditions the difference in boundary colour and spot colour cannot be differentiated. We can use enhanced global contrast to remove the problem of light variation to improve the segmentation performance of the system. We can solve the problem caused by global and automatic thresholding by the use of enhanced global contrast which reduces the need of thresholding itself. The differentiation of boundary colour and spot colour is possible by using increased global contrast.

A detailed review and classification of segmented image for paddy plant disease