Call for Papers:Vol.9 Issue.2

Submission
Last date		 30-Apr-2023
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-9,Issue-2. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  CROP DISEASE DETECTION USING CNN
PaperId: :  19361
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 2 2023
DUI:    16.0415/IJARIIE-19361
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
E.Angel Anna Prathiba ERODE SENGUNTHAR ENGINEERING COLLEGE
S.GobikaERODE SENGUNTHAR ENGINEERING COLLEGE
S.KanishkaERODE SENGUNTHAR ENGINEERING COLLEGE
S.MohanapriyaERODE SENGUNTHAR ENGINEERING COLLEGE

Abstract

COMPUTER SCIENCE AND ENGINEERING
Crop disease, Deep learning, Transfer Learning, MobileNet, InceptionV3, Image classification.
For smallholder farmers, plant diseases are a constant problem that put their livelihood and food security at danger. The prospect for picture categorization in agriculture has been made possible by the current revolution in smartphone adoption and computer vision models. State-of-the-art in image recognition, convolutional neural networks (CNNs) have the capacity to quickly and accurately diagnose a condition. The effectiveness of a pre-trained ResNet34 model for spotting crop disease is examined in this research. The created model can identify seven plant illnesses from healthy leaf tissue and is executed as a web application. For the reason of validating and training the model, a dataset of 8,685 greenery images that were taken in a organised setting is established. The planned may reach an exactness of 97.2%, according to validation data. on top of an F1 total of as a minimum 96.5%. This shows that CNNs can classify plant illnesses technically, and it points the way to AI results for small-scale farmers. Crop illnesses have grown significantly in recent years as a outcome of severe climate changes and a lack of immunity in plants. This results in extensive agricultural destruction,reduces crop production, which eventually costs farmers money. Identification and treatment of the illness have convert a significant difficulty as a outcome of the rapid expansion of a variability of diseases and the farmer's adequate knowledge. The leaves have a similar texture and appearance, which are characteristics for identifying the disease kind. Hence, deep learning combined with computer vision offers a solution to this problem.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. "CROP DISEASE DETECTION USING CNN" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 2 2023 Page 402-410
MLA E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. "CROP DISEASE DETECTION USING CNN." International Journal Of Advance Research And Innovative Ideas In Education 9.2(2023) : 402-410.
APA E.Angel Anna Prathiba , S.Gobika, S.Kanishka, & S.Mohanapriya. (2023). CROP DISEASE DETECTION USING CNN. International Journal Of Advance Research And Innovative Ideas In Education, 9(2), 402-410.
Chicago E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. "CROP DISEASE DETECTION USING CNN." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 2 (2023) : 402-410.
Oxford E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. 'CROP DISEASE DETECTION USING CNN', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, 2023, p. 402-410. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/CROP_DISEASE_DETECTION_USING_CNN_ijariie19361.pdf (Accessed : ).
Harvard E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. (2023) 'CROP DISEASE DETECTION USING CNN', International Journal Of Advance Research And Innovative Ideas In Education, 9(2), pp. 402-410IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/CROP_DISEASE_DETECTION_USING_CNN_ijariie19361.pdf (Accessed : )
IEEE E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya, "CROP DISEASE DETECTION USING CNN," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 2, pp. 402-410, Mar-App 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/CROP_DISEASE_DETECTION_USING_CNN_ijariie19361.pdf [Accessed : ].
Turabian E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. "CROP DISEASE DETECTION USING CNN." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 2 ().
Vancouver E.Angel Anna Prathiba , S.Gobika, S.Kanishka, and S.Mohanapriya. CROP DISEASE DETECTION USING CNN. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(2) : 402-410. Available from: https://ijariie.com/AdminUploadPdf/CROP_DISEASE_DETECTION_USING_CNN_ijariie19361.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Analysis of Image Steganographic techniquesComputer Science EngineeringMadhu R Download
Knowledge Representation and Reasoning: A Review of Current Techniques and Future Directions in AIComputer EngineeringRizwan Abdul Rahim Download
REAL TIME CHATTING APPLICATION FOR NATIVE ORGANIZATIONComputer EngineeringNIRANJAN V Download
AN INTRUSION DETECTION SYSTEM TO PREVENT THE K-ZERO DAY ATTACK PROPAGATIONCOMPUTER ENGINEERING BHARATH A Download
Street Dancers web applicationComputer science Veluguri Bhavitha kumari Download
Base Station Monitoring SystemComputer EngineeringDheenuka M Download
Remote workforce checkin systemComputer EngineeringDhaniesh M Download
DIGITAL CAMPUS QUERY ACQUISITION SYSTEMComputer Scence and EngineeringGOWTHAMA KURINJI VEANDAN R M Download
Hand Gesture Controlled Virtual Mouse Using Artificial Intelligence.Computer Science and EngineeringKAVITHA R Download
REAL TIME DETECTION AND CLASSIFICATION OF YOGA POSE USING TENSORFLOW MOVENETComputer EngineeringJayasurya J Download
CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM FOR REAL ESTATECOMPUTER ENGINEERINGHEMAPRIYA R Download
CROP DISEASE DETECTION USING CNNCOMPUTER SCIENCE AND ENGINEERINGE.Angel Anna Prathiba Download
Customer Segmentation using Tableau MethodComputer Science and EngineeringS.Sivaselvi Download
Bank loan prediction system using Logistic RegressionComputer Science and EngineeringS.Sivaselvi Download
Street Light Monitoring and Controlling SystemComputer Science and EngineeringProf. Virendra Yadav Download
12