Report on plant green house and image processing

8/19/2019 Report on plant green house and image processing

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ABSTRACT:An estimated seventy percent of Indian economy depends on agriculture. Sincethere is growing Indian population, which is increasingly dependent on theagricultural yield, production of the crops must be enhanced. In order to grow morethe diseases must be analyzed in prior. Diseases are analyzed using different image

processing techniques. One such technique is proposed here. he proposedframewor! has been implemented in three phases. "irst, image segmentation is

performed using # means clustering to identify the diseased regions area. In thene$t step features are e$tracted from segmented regions, these feature are then usedfor classification into healthy or disease affected type. In third step data from thesensors and classification results are updated to the server for remote monitoring.

IntroductionImages form important data and information in biological sciences. Digital image

processing and image analysis technology based on the advances inmicroelectronics and computers has many applications in biology and itcircumvents the problems that are associated with traditional photography. hisnew tool helps to improve the images from microscopic to telescopic range andalso offers a scope for their analysis. It, therefore, has many applications in biology%&astogi and 'hadda, ()*)+.. lant diseases cause periodic outbrea! of diseases which leads to large scale deathand famine. It is estimated that the outbrea! of helminthosporiose of rice in northeastern India in ()- caused a heavy loss of food grains and death of a million

people. Since the effects of plant diseases were devastating, some of the cropcultivation has been abandoned. It is estimated that /001 plant disease losses in2eorgia %3SA+ is appro$imately 456 .05 million %7ean, /00)+. In India noestimation has been made but it is more than 3SA because the preventive stepsta!en to protect our crops are not even one8tenth of that in 3SA. he na!ed eyeobservation of e$perts is the main approach adopted in practice for detection andidentification of plant diseases. 9ut, this requires continuous monitoring of e$pertswhich might be prohibitively e$pensive in large farms. "urther, in some developingcountries, farmers may have to go long distances to contact e$perts, this ma!esconsulting e$perts too e$pensive and time consuming %Al8:iary et al., /0((+ andmoreover farmers are unaware of non8native diseases.

Automatic detection of plant diseases is an important research topic as itmay prove benefits in monitoring large fields of crops, and thus automaticallydetect the diseases from the symptoms that appear on the plant leaves. his enablesmachine vision that is to provide image based automatic inspection, processcontrol and robot guidance. 'omparatively, visual identification is labor intensive,



techniques are used to classify the images according to the specific problem athand. "igure ( depicts the basic procedure of the proposed vision8based detectionalgorithm in this paper.

In the initial step, the &29 images of all the leaf samples were pic!ed up. hestep8by8step procedure of the proposed system>

(+ &29 image acquisition?

/+ 'onvert the input image from &29 to :SI format?

+ ;as!ing the green8pi$els?

-+ &emoval of mas!ed green pi$els? .

6+ Segment the components?

5+ Obtain the useful segments?

1+ 'omputing the te$ture features and classify the disease

*+ upload sensor data to the centralized server.



'olor transformation structure

"irst, the &29 images of leaves are converted into :SI color space

representation. he purpose of the color space is to facilitate the specification of colors in some standard, generally accepted way. :SI %hue, saturation, intensity+color model is a popular color model because it is based on human perception%2onzalez and =oods, /00*+. :ue is a color attribute that refers to the dominantcolor as perceived by an observer. Saturation refers to the relative purity or theamount of white light added to hue and intensity refers to the amplitude of thelight. 'olor spaces can be converted from one space to another easily. After thetransformation process, the : component is ta!en into account for further analysis.S and I components are dropped since it does not give e$tra

;as!ing green pi$els

In this step we identify the mostly green colored pi$els. After that, based onspecified threshold value that is computed for these pi$els, the mostly green pi$elsare mas!ed as follows> if the green component of the pi$el intensity is less than the

pre8computed threshold value, the red, green and blue components of the this pi$elis assigned to a value of zero. his is done in sense that the green colored pi$elsmostly represent the healthy areas of the leaf and they do not add any valuableweight to disease identification. "urthermore this significantly reduces the

processing time.&emoving the mas!ed cells

In this step, the pi$els with zeros red, green, blue values were completelyremoved. his is helpful as it gives more accurate disease classification andsignificantly reduces the processing time.

Segmentation>

"rom the above steps, the infected portion of the leaf is e$tracted. heinfected region is then segmented into a number of patches of equal size. he sizeof the patch is chosen in such a way that the significant information is not lost. Inthis approach patch size of /@ /pi$els is ta!en. he ne$t step is to e$tract theuseful segments. ot all segments contain significant amount of information. So

Report on plant green house and image processing

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