A Survey on Different Disease and Image Processing Techniques in Sugarcane Crops

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 11, 2016 | ISSN (online): 2321-0613

A Survey on Different Disease and Image Processing Techniques in Sugarcane Crops Dr.D.Devi Aruna Assistant Professor Department of Computer Applications Dr.N.G.P Arts and Science College Coimbatore Abstract— Sugarcane is an important crop in India and many countries. Everyday human being need to have sugar to enrich their food items. The sugarcane Industry remains one of the main pillars of the Indian economy, though facing many problems. Nowadays there are different diseases which affecting the plants in diverse areas. Sugarcane plant disease detection is emerging field in India as agriculture is important sector in Economy and Social life and useful in supporting optimal sugarcane production. Gradually with technical and scientific advancement, more reliable methods through lowest turnaround time are developed and proposed for early detection of sugar plant disease. Such techniques are widely used and proved beneficial to farmers as detection of plant disease is possible with minimal time span and corrective actions are carried out at appropriate time.

This paper discussed about different disease in sugarcane plant and evaluated existing techniques for detection of sugarcane plant diseases. It helps the researcher to get clear outlook about the techniques and methodologies.

Key words: Sugarcane Plant Disease, Image Processing, Different Diseases, Red Rots I. INTRODUCTION India is an agriculture based country, wherein seventy percent of the population depends on agriculture. Diverse nature of India, uncertainty in rainfall, effect of pests, diseases and stress on the crop can cause decrease in crop production. Sugarcane Industry remains one of the main pillars of the Indian economy, though it is facing many problems. Sugarcane plant disease is one of the important factor causes that reduces quantity and degrades quality of the agricultural products. The management of sugarcane plant requires close monitoring especially for the management of diseases that can affect production significantly and subsequently the post-harvest life. In most of the cases pests or diseases are seen on the leaves or stems of the plant. Therefore identification of plants leaves and finding out the diseases plays a key role in successful cultivation of plants. Disease management is a most challenging task. Mostly diseases are seen on the leaves or stems of the plant. For their easy and minimal way of preventing these diseases different researches carried out in image processing[13].

II. TYPES OF DISEASE There are varieties of disease spots which tend to resemble each other and can easily be confused with one another by inexperienced people. Misunderstanding one spot for another can be quite catastrophic as application of the wrong fungicide will result in loss of money without the plant being treated and allowing more time for the disease to spread further [11].Figure1 shows the classification of sugarcane disease.

Fig. 1: Classification of sugarcane disease A. Red Rot: The disease first appears as red bright lesions on mid rib of leaves and shows itself as drooping and changing of color of upper leaves. Withering of the leaves proceed downwards.

Usually third or the fourth leaf from the top is affected and shows drying at the tip. The pith becomes red and later on brown.

B. Leaf Spot: The disease may be characterized itself on leaves as small lesions, which gradually enlarge along mid rib and assure dark red to brown color. In severe infection, the leaves become dry affecting photosynthesis.

C. Sugarcane Mosaic Virus: Mottling of young crown leaves showing a definite pattern of alternating dark and light green colored patches of varying size and run parallel to the midrib of leaf.

D. Yellow Spot: There exist two types of Yellow Spot.The first type of spot is yellow in color. However, in certain varieties of sugarcane with red stalks, the spots appear as red. Despite the color, both types have the same physical characteristics. They are irregular in shape and dimension. They can vary from minute dots to spots attaining 1 cm in diameter.

E. Brown Spot: Brown spot causes reddish-brown to dark-brown spots on sugarcane leaves. The spots are oval in shape, often surrounded by a yellow halo and are equally visible on both sides of the leaf. The long axis of the spot is usually parallel to the midrib. This spot often tends to be confused with the Ring Spot.

III. LITERATURE REVIEW Many research papers are describing the advancement of image processing for a variety of methodologies.

M. R. Mobasheri et.al [1] used broad band digital camera for sugarcane phonological date estimation. While doing this, it was noticed that the effect of a layer of soil dust on the leaves can limit the capability of the procedure.

A comparison of histogram of RGB bands in each leaf photos before and after the dust removal showed that the effect of the dust on Difference Normalization (DN) values in each of three RGB band scan be estimated. This was done

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A Survey on Different Disease and Image Processing Techniques in Sugarcane Crops (IJSRD/Vol. 3/Issue 11/2016/075)

by calculating the difference between mean DN values in each band before and after dust removal.

LIU Yaju and CAI Zhenjiang [2] developed computer vision system for monitoring plant growth. They concluded that the individual plant could be distinguished from the images by computer image processing. This was done by collecting the growing images of the young plant leaves, measuring biophysical and biochemical indices and counting each colour eigen value. The developed measuring models could be used to measure the relation of full nitrogen content, which was essential for plant growing with chlorophyll.

Sungkur Roopesh K. et.al [13] designed and implemented a reliable and efficient automated system to recognize fungi caused disease spots on sugarcane leaves.

They used algorithms such as the chain code technique, bounding box method and moment analysis and tested for suitability in the recognition of sugarcane diseases. The recognition precision of the implemented system was found to be 95.3%.

K. Moshashi et.al [3] designed algorithm for mechanizing sugarcane planting by machine vision system and image processing method. This algorithm used convolution, thresholding and look-up table operation for identification of sugarcane nodes and sent the cut-point position of two consecutive nodes to microcontroller.

P.Revathi et al [4] developed two phases to identify the affected part of the disease. Initially Edge detection based Image segmentation is done, and finally image analysis and classification of diseases is performed using our proposed Homogeneous Pixel Counting Technique for Cotton Diseases Detection (HPCCDD) Algorithm. The goal of this research work is identify the disease affected part of cotton leaf sport by using the image analysis technique. This work find out the computer systems which analyze the input images using the RGB pixel counting values features used and identify disease wise and next using homogenization techniques Sobel and Canny using edge detection to identify the affected parts of the leaf spot to recognize the diseases boundary is white lighting and then result is recognition of the diseases as output.

H. Al Hiary et al [5] designed to detection of leaf diseases has been used method is threefold: 1) identifying the infected object based upon k-means clustering; 2) extracting the features set of the infected objects using color co-occurrence methodology for texture analysis; 3) detecting and classifying the type of disease using NNs, moreover, the presented scheme classifies the plant leaves into infected and not infected classes. In details, a color transformation structure for the RGB leaf image is created, and then, a device independent color space transformation for the color transformation structure is applied in next step.

After that, the image at hand is segmented using K-Means clustering technique. This all step are determined the infected object(s) and identify the mostly green colored pixels. After that, based on specified and varying threshold value that is computed performed to the extracted features through a pretrained neural network.

S. Arivazhagan et al [7] developed four main steps are first a color transformation structure for the input RGB image is created, and then the green pixels are masked and removed using specific threshold value followed by

segmentation process, computing the texture features using color co-occurrence method for the useful segments, finally the extracted feature are passed through the classifier.

Support vector machines are a set of related supervised learning method used for classification and regression. The detection accuracy is improved by SVM classifier. The two class problem is then extended to multiclass problem where the detected leaf diseases are then classified into various categories. By this method, the plant diseases can be identified at initial stage itself and the pest control tools can be used to solve pest problems while minimizing risks to people and the environment.

Shen Weizheng et al [8] analyzed image segmentation and leaf region was segmented by using Otsu method. In the HSI color system, H component was chosen to segment disease spot to reduce the disturbance of illumination changes and the vein. Then disease spot regions were segmented by using Sobel operator to examine disease spot edges. Finally plant diseases are graded by calculating the quotient of disease spot and leaf areas.

Mrunalini et al [9] developed two techniques for feature extraction and comparison of two techniques. Otsu Threshold: thresholding creates binary image from gray level ones by turning all pixels below some threshold to zero and all pixels about that threshold to one. K-Means clustering is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data.

Joanna Sekulska et al [10] describes the segmentation consist in image conversion to HSV color space and fuzzy c-means clustering in hue-saturation space to distinguish several pixel classes. These classes are then merged at the interactive stage into two final classes, where one of them determines the searched diseased areas.

IV. CONCLUSION Sugarcane is one of the most important foods in the world.

Everyday human being need to have sugar to enrich their food items. In all other variety of food items the sugarcane plays vital role in the agriculture. Nowadays there are different diseases which affecting the plants in diverse areas.

The literature survey done in this paper provides a new insight in detection of the diseases of sugarcane plant. The scope in doing research in this field is to design such system that automatically estimating the rigorousness of the detected disease.

REFERENCES [1] Mobasheri, M Chahardoli & M Farajzadeh “Sugarcane phenological date estimation using broadband digital cameras” American Jornal of Agricultural and Biological Science 3 (2), 468-475.

[2] LIU Yaju & Zhenjiang “A Research in the Application of Computer-Vision to Plant Growth Monitoring” School of Mechanical and Electrical engineering, Heei Agricultural University, Baoding, Hebei,2010.

[3] K. Moshashai, M. Almasi, S. Minaei & A.M. Borghei,”Identification of Sugarcane Nodes Using Image Processing and Machine Vision Technology”International Journal of Agricultural Research, 3: 357-364. 2008.

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[4] P.Revathi & M.Hemalatha, “Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques” ISBN, pp169-173, 2012 IEEE. [5] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik & Z. ALRahamneh, “Fast and Accurate Detection and Classification of Plant Diseases” IJCA, vol. 17(1), pp.

31-38, March 2011, IEEE-2010.

[6] Piyush Chaudhary, Anand K. Chaudhari, Dr. A. N. Cheeran & Sharda Godara, “Color Transform Based Approach for Disease Spot Detection on Plant Leaf”, IJCST, 3(6), pp.65-70June 2012.

[7] S.Arivazhagan, R. Newlin Shebiah, S. Ananthi & S. Vishnu Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features”,CIGR, vol. 15(1), pp.211-217, March 2013.

[8] Shen Weizheng, Wu Yachun, Chen zhanliang & Wei Hongda, “Grading Method of Leaf Spot Disease Based on Image Processing” ICCSS, pp.491-494, 2008 IEEE. [9] Mrunalini R. Badnakhe, Prashant & R. Deshmukh,“Infected Leaf Analysis and Comparison by Otsu Threshold and k-Means Clustering” IJARCSSE,vol. 2(3), pp.449-452, 2012.

[10] Joanna Sekulska-Nalewajko & Jaroslaw Goclawski, “A semi-automatic method for the discrimination of diseased regions in detached leaf images using fuzzy cmeans clustering”. Polyana-Svalyava (zakarpattya), Ukraine: pp.172-175, 2011 IEEE. [11] Prof.

Sanjay B. Dhaygude &Mr.Nitin P.Kumbhar,“Agricultural plant Leaf Disease Detection Using Image Processing” IJAREEIE, vol. 2(1),pp. 599602, January 2013 [12] Dheed Al Hiary, S. Bani-Ahmad & M. Braik, “A Framework for Detection and Classification of Plant Leaf and Stem Diseases”, pp. 113-118, 2010 IEEE. [13] Roopesh Kevin Sungkur, Sunilduth Baichoo & Aroun Poligadu “An Automated System to Recognise Fungicaused Diseases on Sugarcane Leaves” Proceedings of Global Engineering, Science and Technology Conference ,Singapore, 3-4 October 2013.

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