INVESTIGATIONS ON COCONUT TREE DISEASE SEGMENTATION USING INTELLIGENT TECHNIQUES

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056

Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p ISSN: 2395 0072

INVESTIGATIONS ON COCONUT TREE DISEASE SEGMENTATION USING INTELLIGENT TECHNIQUES

1Student, Dept. of ECE, GCE, Tirunelveli, Tamil Nadu, India 2Professor, Dept. Of ECE, GCE, Tirunelveli, Tamil Nadu, India ***

Abstract - Coconut is one of India's most flexible plantation crops. It plays a unique role in Indian culture and heritage. Every portion of the coconut plant can be used for a variety of purposes. Using image processing techniques, this research focuses on detecting problems in coconut trees such as stem bleeding, leaf blight disease, and pest invasion by the red palm weevil. The segmentation of diseased regions provides advance warning of the disease and increases coconut production. K Means Clustering and Fuzzy clustering segmentation methods are employed in this paper and clustering are used to compute the affected area.

Key Words: Stem bleeding, Leaf Blight, Segmentation, K Means, Fuzzy C Means.

1. INTRODUCTION

Coconut tree (Coco’s nucifera) is the only extant speciesofthegenusCoco’sandbelongstothepalmfamily (Arecaceae). The name "coconut" can refer to the entire coconutpalm,theseed,orthefruit,whichisadrupe,nota nut, according to botanical classification. After the three indentationsonthecoconutshellthatmimicfacialfeatures, thenamecomesfromtheOldPortuguesewordcoco,which means"head"or"skull."Theyareaculturalemblemofthe tropicsandcanbefoundincoastaltropicalregions.

1.1.1 Plant

Coco’snuciferaisapalmthatmayreachaheightof 30meters(100feet)andhaspinnateleavesthatare60 90 cm(2 3feet)long.Atallcoconutpalmtreecanproduceupto 75fruitsperyearinfertilesoil,althoughitusuallyproduces lessthan30.Coconutpalmsproducetheirfirstfruitinsixto ten years with correct care and growing circumstances, requiring15to20yearstoreachoptimumproduction

1.1.2 Fruit

The coconut fruit is a drupe, not a real nut, accordingtobotany.Ithasthreelayers,sameasotherfruits: exocarp,mesocarp,andendocarp.Theglossyouterskinof theexocarpisyellow greentoyellow brownincolor.The mesocarpismadeupofafibreknownascoir,whichhasa variety of traditional and commercial applications. The coconut's"husk"ismadeupoftheexocarpandmesocarp, whilethehard"shell"ismadeupoftheendocarp.

1.2 Major diseases affecting coconut tree

Ganoderma BasalStemRot

ButRot

LeafBlight

StemBleeding

Root(Wilt)Disease

LeafRot

1.2.1 Leaf Blight

Theemergenceofyellowishbrownsmalldotson theleafletisoneofthedisease'searlysigns.Thesespots expandandbecomegreywithtime.Thespot'speriphery turnsadarkbrowncolor.Duringwetseasons,bacterial leafblightisadevastatingdiseasethataffectscoconuts.

Fig.1: Leaf Blight

1.2.2 Stem Bleeding

Theexudationofadarkreddishbrownliquidfrom longitudinal fissuresinthe bark and wounds on thestem, which trickles down for several inches to several feet, is knownasstembleeding.Asthediseaseadvances,thelesions spreadhigher.

Fig.2: Stem Bleeding

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056

Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p ISSN: 2395 0072

1.2.3 Red palm weevil

Tunnelinginthestemorbaseofthefronds,leaves withstraightedgesratherthanpointedtips,frassattunnel entrances,witheringanddeathoftheleaves,particularlyat thecrown.

2.3.1 K Means Clustering

TheK meansalgorithmisanunsupervisedmethod forseparatingtheinterestareafromthebackground.Based on the K centroids, it clusters or partitions the given data intoK clustersorsections.Whenyouhaveunlabeleddata, thealgorithmisused(i.e.datawithoutdefinedcategoriesor groups).Thepurposeistolocatespecificgroupsbasedon some form of data similarity, with K representing the number of groups. The goal of K Means clustering is to reducethesumofsquareddistancesbetweenallpointsand theclustercentretothesmallestpossiblevalue.

2.3.1.1 Steps in K Means Algorithm

1.DeterminethenumberofclusterstobeK.

2.ChoosethecentroidsatrandomamongKlocations.

2. PROPOSED METHODOLOGY

Calculatethesegmentationoftheaffectedregionof the coconut tree illness from the disease image. By calculatingtheafflictedarea,thestageofthediseasecanbe determinedquickly.

2.1 Dataset

The images for this project were taken from the TNAU database, which is open to the public. A total of 60 images were chosen for the proposed method's implementation.

2.2 Pre processing

Theinputimagesareconvertedasasuitableformat forthefurtherprocessing.

2.2.1. Image Enhancement

The contrast of the input images is increased by adjusting them. Because the input coconut images were takenonasunnyday,increasingthecontrastisnecessary for further processing. Lab color space was applied to the RGB images. The same information is converted to a lightnesscomponentL*andtwocolorcomponentsa*andb* inlab.Lightnesswascreatedtomimichumanvision,which ishighlysensitivetogreenbutnotsomuchtoblue.Because green color is prevalent in coconut images, the lightening techniquemakesthegreencolorsensitive.

2.3 Image Segmentation

Image segmentation is a technique for breaking downadigitalimageintosubgroupsinordertoreducethe image'scomplexityandmakefutureprocessingoranalysis of the image easier. In simple terms, segmentation is the process of assigning labels to pixels. The focus of this researchwasonK MeansclusteringandtheFuzzyC Means Clusteringmethod.

3.Assigneachdatapointtothecentroidsthatareclosestto it,formingKclusters.

4.Calculateandpositioneachcluster'snewcentroids.

5.Assigneachdatapointtothenewcentroidsthatare closesttoit.

Figure4showstheflowdiagramforK Means Clusteringsegmentation.

ImageEnhancement

CoconutTreeImages

ClusteringImage Calculatingthe DiseaseAffectedarea

Fig.4: Flow Diagram of K Means Clustering

2.3.2 Fuzzy C Means Segmentation

The data must be handled in Fuzzy C Mean by assigning a partial membership value to each pixel in the image.Thefuzzyset'smembershipvalueisintherangeof0 to 1. A member of one fuzzy set can also be a member of otherfuzzysetsinthesameimageinfuzzyclustering.When amemberfunctionisusedtocharacterisesomething,ithas three basic characteristics. The fuzzy set's core is the full member, Support is the set's non membership value, and Boundaryisthepartialmembershipwithavaluebetween0 and1.Ingeneral,determiningwhetherapixelbelongstoa region is difficult. This is owing to the region's unsharp transitions.

2.3.2.1 Fuzzy c mean Algorithm steps

1. Calculatetheprototypeclusters(means).

2.Calculatethedistancesbetweenthepoints.

3.Makechangestothepartitionmatrix

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056

Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p ISSN: 2395 0072

3.2 Fuzzy C Means Segmentation outputs

4. Conclusion

The disease of the coconut tree, such as stem bleeding,leafblightdisease,andpest infectionbytheRed palm weevil, were segmented in this study. For testing, a total of roughly 60 images were taken from the publically availableTNAU,both normal anddisease affectedimages. Thedisease affectedarearegionsweresegmentedfromthe totalimageusingtheK Meanssegmentationtechnique.The disease affected area is also computed using K means. To improve the segmentation outcome, Fuzzy C Means segmentation was employed to increase the number of iterationsandclusters.MATLAB2018awasusedtoproduce the above segmentation. The disease affected region was alsosegmentedandthedisease affectedareawasestimated usingthesesegmentations.

REFERENCES

[1]P.Balamurugan,R.Rajesh,“NeuralNetworkBasedSystem fortheClassificationofLeaf RotDiseaseinCocosNucifera TreeLeaves”,EuropeanJournalofScientificResearch,ISSN 1450 216XVol.88No1October,2012,pp.137 145.

[2]P.Revathi, M.Hemalatha, “Classification of Cotton Leaf Spot Diseases Using Image Processing Edge Detection Techniques”,InternationalConferenceonEmergingTrends inScienceEngineeringandTechnology,ISBN:978 1 4673 5144 7/12,2012

[3]SanjayB.Dhaygude,Mr.Nitin,P.Kumbhar,“Agricultural plant Leaf Disease Detection Using Image Processing”, InternationalJournalofAdvancedResearchinElectricaland IntrumentationEngineeringVol.2,Issue1,January2013

[4]RongZhou, Shun’ichi Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu “Early Detection and Continuous Quantization of Plant Disease Using Template MatchingandSupportVectorMachineAlgorithm”,2013First International Symbosium on Computing and Networking DOI10.1109/CANDAR.2013.52.

[5]Shital Bankar, Ajita Dube,Pranali Kadam, Prof. Sunil Deokule“PlantDiseaseDetectionTechniquesUsingCanny Edge Detection & Color Histogram in Image Processing“ ,InternationalJournalofComputerScienceandInformation Technologies,Vol.5(2),2014,1165 1168.

[6]Dr.K.Thangadurai,K.Padmavathi“ComputerVisionimage Enhancement For Plant Leaves Disease Detection“,2014 World Congress on Computing and Communication Technologies,DOI10.1109/WCCCT.2014.39

[7]RajleenKaur,Dr.SandeepSinghKang“AnEnhancementin ClassifierSupportVectorMachinetoImprovePlantDisease Detection”2015 IEEE 3rd International Conference on MOOCs,InnovationandTechnologyinEducation(MITE).

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 06 | Jun 2022 www.irjet.net p ISSN: 2395 0072

[8]Sachin D. Khirade, A.B. Patil “Plant Disease Detection UsingImageProcessing”,2015InternationalConferenceon ComputingCommunicationControlandAutomation.

[9]Ani Dath, M.Balakrishnan “Expert System on Coconut Disease Management and Variety Selection” International Journal of Advanced Research in Computer Science and SoftwareEngineering,Volume6,Issue3,March2016

[10] Yang Zhang, Chenglong Song, Dongwen Zhang “Deep Learning basedObjectDetectionImprovementforTomato Disease” Hindawi Publishing Corporation Computational IntelligenceandNeuroscience,Volume2016.

[11]JaymeGarciaArnalBarbedo,LucianoVieira Koenigkan,ThiagoTeixeiraSantos“Identifyingmultiple plantdiseasesusingdigitalimageprocessing”,ELSEVIER 1537 5110,2016

[12]SujathaR,YSravanKumarandGarineUmaAkhil“Leaf diseasedetectionusingimageprocessing”,Journalof ChemicalandPharmaceuticalSciences,March2017

[13]Malti K. Singh , Subrat Chetia “Detection and Classification of Plant Leaf Diseases in Image Processing using MATLAB”, International Journal of Life Sciences Research,Vol.5,Issue4,pp120 124),December2017

[14]JaymeGarciaArnalBarbedo“Impactofdatasetsizeand variety on the effectiveness of deep learning and transfer learning for plant disease classification”, ELSEVIER 0168 1699,August2018

[15]GuilingSun,XinglongJia,TianyuGeng“PlantDiseases RecognitionBasedonImageProcessingTechnology”,Journal of Electrical and Computer Engineering, Volume 2018,DOI:10.1155/2018/6070129

[16] Jayme Garcia Arnal Barbedo “Plant disease identificationfromindividuallesionsandspotsusingdeep learning”ELSEVIER,1537 5110,2019

[17]Dr.AbrahamChandy“Pestinfestationidentificationin coconut trees using deep learning”, Journal of Artificial Intelligence and Capsule Networks (2019), Vol.01/No.01,DOI:http://doi.org/10.36548/jaicn.2019.1.002

[18]S.Siddesha, S.K.Niranjan.“DetectionofMiteDiseaseand ComputationofAffectedAreainRawCoconut”,International JournalofEmergingTechnologies11(2):08 12(2020).

[19]Dr.A.Anushya “Identifying Infected Coconut trees via Classification”,InternationalJournalofAdvancedResearch in Computer Science and Electronics Engineering(IJARCSEE),Volume9,Issue2,February 2020.

[20]H.D.Gadade,Dr.D.K.Kirange“MachineLearningBased IdentificationofTomatoLeafDiseasesatVariousStagesof

Development”, Proceedings of the Fifth International Conference on Computing Methodologies and Communication(ICCMC2021).

[21] Shrihari Kallapur, Mahith Hegde, Adithya D. Sanil, Raghavendra Pai, Sneha NS “Identification of aromatic coconuts using image processing and machine learning techniques”,GlobalTransitionsProceedings,August2021

© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page2485