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A Survey-Plant Leaf Disease in Horticulture Using Classification Algorithm

N.Ramya1* ,Dr.S.Mohana2

1P.G student , Dept. of Computer Science &Engg , Saranathan college of Engineering,Tamilnadu,INDIA

2Assistant Professor, Dept. of Computer Science & Engg , Saranathan college of Engineering,Tamilnadu,INDIA Abstract.

Plants are viewed as a basic asset due to the numerous ways they bolster life on Earth. Plants discharge oxygen from their leaves, which people and different creatures need to relax. Living things need plants to live and it likewise assists with cleaning water as well. Illness can assault the plant whenever before collecting. Hence leaf infection recognition assumes a significant job in agribusiness field. A definitive point is to build the quality and size of the creation rate by decreasing the expense and pesticides utilization in the field. The highlights and properties of leaf like size, shape and surface are mulled over for grouping. For distinguishing among sound and infected leaf from the dataset this paper utilizes Random Forest and methods in AI for leaf illness ID.

Key Words: Healthy and diseased leaf, feature extraction, Random forest, decision tree, K-means clustering.

1. Introduction

India is notable for farming and it is the spine for nation improvement. Anyway horticulture fields face numerous issues in crop creation. One of the significant explanations behind the misfortune in the creation is plant leaf infection and it is hard to recognize as well. The majority of the plants are influenced by parasitic, infection and bacterial sicknesses. Climatic condition is additionally the purpose behind plant illness. In times past the rancher needs to legitimately look at each leaf, regardless of whether it is influenced or not.Theseunaided eye perception techniques are tedious, off base, include tremendous labor and not suitable for bigger fields. Additionally information about the nuisance the executives, infections of different harvests. Poisonous microbe, climatic changes and helpless sickness control are the significant reason for the misfortune in crop creation.Henceforth, AI gives different expectation systems to distinguishing plant leaves sickness. The different sizes, shape, surface on leaf are useful in identifying the malady. Irregular Forest Used for both order and relapse. It is an outfit of randomized choice trees, increments prescient intensity of the calculation and furthermore forestalls over fitting. Arbitrary woods is the most straightforward and broadly utilized calculation.

“Figure 1Block diagram of system”

Input image is

given

Extracting the feature from the

image

Database Image is

pre processed

Classification algorithm

Output Displaying the

predicted disease

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2. LITERATURE SURVEY

This segment depicts about work done by various specialists in various zone, for example, grouping of plants, separating the highlights and so on…

"Acknowledgment of jute disease by leaf image and classification using convolutional neural network"- (Zahidhasan et al., 2019). This paper manages distinguishing distinctive malady that influences jute plant by utilizing profound learning. by utilizing the dataset these model recognize the solid and undesirable jute leaf. fundamental reason for this paper is to give more exactness.

“Machine learning for plant leaf disease detection and classification”-(Sherlypusphaannabel et al., 2019).Which depends on foreseeing different plant leaf illness and distinguishing the malady with the assistance of various ai procedures like counterfeit neural system, bolster vector machine.the given info leaf is arranged dependent on the component, shading and power and afterward the characterization is surrendered to ai calculation for indentifying the infection in plant leaf.

“Prediction of potato disease from leaves using deep convolutional neural netwokrs towards a digital agriculturemsystem”- (Al-amin et al., 2019),here in this anticipating the potato infection in leaf.the preferred position is that the framework is less expense and less tedious and gives more precision in anticipating just the malady in potato plant leaf.

“Detection of unhealthy region of plant leaves using texture features”-(Malini and Rathajeyalakshmi, 2015). This paper comprises of four fundamental advances, the initial step is changing over the information picture into rgb picture and the subsequent advance is concealing the green pixels and are evacuated by division procedure and afterward at long last the highlights of leaves are removed which is given to classifier.

“Image processing and classification a method for plant disease detection”- (Simranjeetkaur et al., 2019). In this model the info information picture is given to the framework, which performs division process that distinguishes the influenced bit of the leaf and the element is extricated from the sectioned stage. subsequent to applying the order calculation for identifying between the solid and infections leaf.

“Plant disease detection techniques”-(Ufaq khan and Ashishoberoi, 2019). This paper manages two stage which is division and highlight extraction. in division stage the infected segment from the leaf is distinguished and recognized.

at that point the extricated highlight of leaf is surrendered to classifier for recognize the sickness in the leaf. This paper utilizes picture handling procedure for grouping.

“Image processing based rice plant leaves diseases”-(Gayathridevi and Neelamegam, 2019),in this methodology the picture is taken from the field and the element is extricated from the picture gathered. In the event that any clamor in the picture will be evacuated before applying arrangement calculation.

“Plant disease identification and classification through leaf images”- (Kaur et al., 2018).Which depends on distinguishing different infection of leaf, for example, microbes, growth and viral ailment.in this model the picture is preprocessed, division and the component is extricated. there are two stage in this model, which is preparing and testing. handling, extraction will be done in preparing stage. in division module it recognizes the contaminated area from the leaf. furthermore, in testing stage the testing picture will be given and the classifiers distinguish whether the leaf is solid or sick one.

“plant disease detection using machine learning algorithm”-(Ramesh et al., 2018). This methodology manages preprocessing, feature extraction, training of classifier and classification. the assortment of ailing and sound leaf are prepared by utilizing arbitrary timberland procedure and for separating the highlights of leaf utilizing histogram oriented gradient (hog).there are three segments in hog, they are hu minutes, haralick surface and shading histogram.

“Plant disease prediction using machine learning algorithms”-(Prem rishi kranth et al., 2018).This paper manages distinguishing plant leaf malady by applying different ai calculation, for example, choice tree, naïve bayes, fake neural system and k implies bunching and random woodland method is utilized to prepared the arranged leaves and foreseeing the distinctive infection of the leave.

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“Plant disease detection and its solution using image classification”, (Saradhambal et al., 2018). Here identifying the leaf sickness and giving the recuperation arrangement by utilizing picture handling strategy. it will show the influenced piece of the leaf in rate the benefit of this model is that they are utilizing voice route framework which will be extremely helpful for an individual even with less involvement with taking care of programming.

“Applying image processing technique to detect plant diseases”-(Anandkulkarni and Ashwinpatil, 2012).This methodologies is with catching the picture which is separated and fragmented utilizing gabor channel and by utilizing the counterfeit neural system recognize the sound and sick leaf and by utilizing this calculation the given information picture is grouped, the malady is accurately anticipated.

“A review of off-line leaf recognition using neural network”-(Needasamreen khan and Rajesh pandhare2012).This overview is perceive the kind of leaf by utilizing neural system. The info picture of the leaf is given which is prepared, highlights are extricated from the leaf and the arranged information about leaf is given to the neural system calculation, which is utilized to recognize the sound and undesirable leaf.

“An automatic plant leaf type detection”-(Vijayalakshmi and Mohan, 2016).Different leaf type is thought about, cellular automata (ca) channel method is utilized here for preparing the picture and in division stage the commotion is expelled from the picture. by utilizing bolster vector machine the picture is grouped

TABLE 1 TABLE OF COMPARISON

S..no Authors Journal

and year

Approaches used

Algorithm Out- come 1.

Md. ZahidHasan, Md.

SazzadurAhamed, AniruddhaRakshit, K. M.

ZubairHasan (2019)

IEEE- 2019

This approach is based on identifying and

classifying a jute leaf disease.

Convolution and Neural Network(CNN)

Maximum accuracy of 96%.

2. L. SherlyPusphaAnnabe, T.

Annapoorani and P.

Deepalakshmi (2019)

IEEE- 2019

Various leaf diseases is detected based on feature selection.

Artificial Neural Network (ANN),

Support Vector Machine (SVM).

Accuracy is found to be 89%.

3. Md. Al-Amin, TasfiaAnikaBushra, MdNazmulHoq (2019)

IEEE- 2019

Predicting different disease on potato leaf

Convolution and Neural Network(CNN)

Provides an accuracy of 98.33%.

4. S. Malini, T.

RathaJeyalakshmi (2019)

IJCSE- 2019

Converting into RGB image and masking a green pixel.

Transformation, detection, classifier.

Provides an accuracy of 83.2%.

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5. Simranjeetkaur,

GeetanjaliBabbar, Gagandeep (2019)

IJITEE- 2019

Identifying the disease based on feature extraction and segmentation.

K means algorithm.

Accurateness of this method is around 97%.

6. UfaqKhan,AshishOberoi (2019)

IJCSMC- 2019

Extraction of feature from the leaf and segmentation process to detect the diseases portion of the leaf.

Image processing technique.

Identify various leaf diseases.

7. T. Gayathri Devi, P.

Neelamegam (2018)

Springer- 2018

Identifying different disease in rice plant

K means and support vector machine(SVM)

This method provides accuracy up to 98.63%

8. SukhvirKaurShreelekhaPandey ,ShivaniGoel (2018)

Springer- 2018

Identifying the bacteria, fungal, virus disease affect on leaf.

K means and support vector machine(SVM)

Reported an accuracy of 87%.

9. Shima Ramesh, Niveditha M, Pooja R, Prasad Bhat N, ShashankN (2018)

IEEE- 2018

Preprocessing, Feature extraction, Training of classifier and Classification

Random forest, Histogram Oriented Gradient (HOG).

This model provides an accuracy of 70%.

10. Prem Rishi Kranth, M. Hema Lalitha, Laharika

Basava,Anjali Mathur (2018)

IJCA- 2018

Identifying various leaf diseases in various plant leaves.

Naive Bayes, Artificial neural network, random forest, k means clustering

Provides an accuracy of 89.93%.

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11. Saradhambal.G , Dhivya.R, Latha.S, R.Rajesh (2018)

IJPAM- 2018

Disease affected leaf is identified and providing a recovery solution.

Image processing

Voice navigation system helps the less expertise person to handle software.

3. Conclusions

In this paper, it is inferred that the method which is utilized for distinguishing the ailment from the leaf is plant sickness recognition procedure. The significant explanation behind identifying the plant ailment is expanding the quality and amount of yield and in this manner lessening the use of pesticides and cost of harvests in the field. The highlights and properties of leaf like size, shape and surface are contemplated for order. In this paper Random Forest procedure is utilized for preparing and AI calculation is utilized for characterization between the solid and unfortunate leaves.

References

1. Al-Amin, Md.,TasfiaAnikaBushra, and NazmulHoq. Md. (2019) “Prediction of potato disease from leaves using deep convolution neural network towards a digital agricultural system”, IEEE, 1st International Conference on Advances in Science, Engineering and Robotics Technology, pp.1- 5.http://doi: .."..10.1109/ICASERT.2019.8934933

2. Anand. H.,Kulkarni, I. and AshwinPatil. R.K. (2012), “Applying image processing technique to detect plant diseases”,International Journal of Modern Engineering Research (IJMER),Vol. 2, no.5, pp. 3661- 3664.http://www.ijmer.com/papers/Vol2_Issue5/DN2536613664.pdf.

3. Gayathri Devi, T. and Neelamegam, P. (2019) “Image processing based rice plant leaves diseases”, - 315256Z2482225306965Cluster Computing, Vol. 22, no.5, pp.1-14.http//:doi:: .--.--10.1007/s10586-018- 1949-x.

4. Kaur, S., Pandey, S. and Goel, S. (2018) “Plants Disease Identification and Classification Through Leaf Images: A Survey”, Archives of Computational Methods in Engineering,pp.1-14. .---090766M22003124483.-- -http://DOI: 10.1007/s11831-018-9255-6

5. Malini, S., and RathaJeyalakshmi, T. (2015) “Detection of Unhealthy Region of Plant Leaves Using Texture Features”,International Journal of Computer Sciences and Engineering, Vol.7, no.8.http://doi: .."..10.1109/ICACEA.2015.7164858

6. NeedaSamreen, Khan, I., Rajesh, and B.Pandhare, B. (2012) “A review on off-line leaf recognition using neural network”,International Journal of Computer Science and Mobile Computing, Vol. 4, no. 1, pp. 478-482 7. PremRishi Kranth, M., HemaLalitha,LaharikaBasava,AnjaliMathur. (2018) “Plant Disease Prediction using

Machine Learning Algorithms”,International Journal of Computer Applications , Vol. 182, no. 25.

8. Pushkara Sharma,Pankaj Hans,Subhash Chand Gupta,”Classification of plant leaf disease using machine learning and image preprocessing techniques”,IEEE 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 978-1-7281-2791-0/20/$31.00 #2020

9. Ramesh, S., Niveditha, M., Pooja, R., Prasad Bhat, N. and Shashank.N. (2018) “Plant disease detection using machine learning”, International Conference on Design Innovations for 3Cs Compute Communicate Control,

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pp. 41-45.http//:Doi..3.".3.10.1109/ICDI3C.2018.00017.

10. Saradhambal, S., Dhivya, R., Latha. S. and Rajesh, R. (2018) “Plant disease detection and its solution using image classification”,International Journal of Pure and Applied Mathematics, Vol. 119, no.14, pp. 879- 884.https://acadpubl.eu/hub/2018-119-14/articles/2/109.pdf.

11. Sherly Puspha Annabel, L.,Annapoorani,T. and P. Deepalakshmi. P.(2019) “Machine Learning for Plant Leaf Disease Detection and Classification”, IEEE- International Conference on Communication and Signal Processing, Vol. l, pp. 4-6, .."..http://doi: 10.1109/ICCSP.2019.8698004.

12. Simranjeetkaur, GeetanjaliBabbar, andGagandeep.(2019) “Image processing and classification a method for plant disease detecion”,International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol.8, no. 9S.https://ijitee.org/wp-content//v8i9S/I11390789S19.pdf

13. UfaqKhan, andAshishOberoi. (2019) “Plant Disease Detection Techniques”,International Journal of Computer

Science and Mobile Computing, Vol.8, no.4,pp.59-

68.https://ijcsmc.com/docs/papers/April2019/V8I4201906.pdf.

14. Vijaya Lakshmi, B. and Mohan.V. (2016) “An automatic plant leaf type detection”, Computers and Electronics in Agriculture, Vol. 125, pp. 99–112, 2016.https://doi.org/10.1016/j.compag.2016.04.033

15. ZahidHasan, Md.,SazzadurAhamed, AniruddhaRakshit.andZubairHasan, K.M. (2019) “Recognitionof jute diseases by leaf image classification using convolutional neural network” 10thInternational Conference on

Computing, Communication and Networking Technologies, pp.1-5.

...."...http://doi. 10.1109/ICCCNT45670.2019.8944907

16. KAKARLA, LAVANYA, and CHAKRAVARTHY RAMA. "SYNTHESIS OF SILVER NANOPARTICLES FROM DIFFERENT PLANT LEAF EXTRACTS AND ITS CRITICAL ANALYSIS USING UV- SPECTROSCOPY." International Journal of Nanotechnology and Application (IJNA) 5.3 (2015) 21-26 17. Singh, Ningthoujam Tiken, Harjeet Singh, and Mutum Shyamkesho Singh. "EVALUATION OF DIFFERENT

FUNGICIDES AND PLANT EXTRACTS FOR MANAGEMENT OF LEAF RUST OF QUERCUS SERRATA THUNB CAUSED BY CRONARTIUM QUERCUUM MIYABE EX SHIRAI." International Journal of Applied and Natural Sciences (IJANS) 7.2 (2018) 25-32

18. BARIŞ, ÇIĞDEM ÇINGIL, and S. E. R. A. P. SAĞLAM-ÇAĞ. "THE EFFECTS OF BRASSINOSTEROIDS ON SEQUENTIAL LEAF SENESCENCE OCCURRING IN GLYCINE MAX L." International Journal of Bio-Technology and Research (IJBTR) 6.4 (2016) 7-16

19. MALIK, SAIRA, S. Y. E. D. SAS BISWAS, and DHIRAJ KUMAR. "SEASONAL VARIATION IN LEAF EXTRACTS OF DENDROCALAMUS STRICTUS SPS-AS REFLECTED IN DIFFERENTIAL EXPRESSION OF SOLUBLE BIOSYNTHATES." International Journal of Botany and Research (IJBR) 7.4 (2017) 1-8

20. Juliyana, S. Jebarose, et al. "Finite Element Analysis of Mono Composite Leaf Spring of Varying Thickness and Varying Width used in Automotives." Int Journal of Mechanical and Production Engineering Research and Development (IJMPERD) 7.6 (2017): 247-254.

21. KIRAN, K. MIRAJKAR, K. PAVITHRA, and S. BIRADAR SUMA. "A STUDY OF DEFENSIVE ENZYMES AGAINST LEAF RUST (PUCCINIA TRITICINA ERIKS) INFECTION AND MOLECULAR SCREENING FOR LEAF RUST RESISTANT GENES IN DICOCCUM WHEAT (TRITICUM DICOCCUM) GENOTYPES." International Journal of Agricultural Science and Research (IJASR) 9.4 (2019) 69-84

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