research-article

An Integrated Digital Image Analysis System for Detection, Recognition and Diagnosis of Disease in Wheat Leaves

Authors:

Diptesh Majumdar,

Dipak Kumar Kole,

Aruna Chakraborty,

Dwijesh Dutta MajumderAuthors Info & Claims

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Pages 400 - 405

https://doi.org/10.1145/2791405.2791474

Published: 10 August 2015 Publication History

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Abstract

Wheat leaves need to be scouted routinely for early detection and recognition of rust diseases. This facilitates timely management decisions. In this paper, an integrated image processing and analysis system has been developed to automate the inspection of these leaves and detection of any disease present in them. Disease features of wheat leaves have been extracted using Fuzzy c-means Clustering algorithm and disease detection, recognition of its type and identification algorithm has been developed based on artificial neural network (ANN). Through the use of ANN and more specifically multilayer perceptrons, detection of the presence of disease in wheat leaves have been successful in 97% of the cases, after analysis of about 300 test images of wheat leaves. Also, identification of type of disease, if present, in wheat leaf has been successful in 85% of the cases.

References

[1]

R. C. Gonzalez. Digital image processing. Pearson Education India, 2009.

Digital Library

Google Scholar

[2]

V. A. Gulhane and A. A. Gurjar. Detection of diseases on cotton leaves and its possible diagnosis. International Journal of Image Processing, 5(5):590--598, 2011.

Google Scholar

[3]

D. Majumdar, A. Ghosh, D. K. Kole, A. Chakraborty, and D. D. Majumder. Application of fuzzy c-means clustering method to classify wheat leaf images based on the presence of rust disease. In Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, pages 277--284. Springer, 2015.

Crossref

Google Scholar

[4]

D. Majumdar, D. K. Kole, A. Chakraborty, and D. D. Majumder. Review: Detection & diagnosis of plant leaf disease using integrated image processing approach. International Journal of Computer Engineering and Applications, 6.

Google Scholar

[5]

J. Patil and R. Kumar. Feature extraction of diseased leaf images. Journal of Signal & Image Processing, 3(1), 2012.

Google Scholar

[6]

J. K. Patil and R. Kumar. Advances in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research, 2(2):135--141, 2011.

Google Scholar

[7]

J. K. Patil and R. Kumar. Color feature extraction of tomato leaf diseases. International Journal of Engineering Trends and Technology, 2(2):72--74, 2011.

Google Scholar

[8]

S. B. Patil and S. K. Bodhe. Betel leaf area measurement using image processing. International Journal on Computer Science and Engineering, 3(7):2656--2660, 2011.

Google Scholar

[9]

S. B. Patil and S. K. Bodhe. Image processing method to measure sugarcane leaf area. International Journal of Engineering Science and Technology, 3(8):6394--6400, 2011.

Google Scholar

[10]

S. B. Patil and S. K. Bodhe. Leaf disease severity measurement using image processing. International Journal of Engineering and Technology, 3(5):297--301, 2011.

Google Scholar

[11]

S. Phadikar, J. Sil, and A. Das. Classification of rice leaf diseases based on morphological changes. Int J Geogr Inf Sci, 2:460--463, 2012.

Google Scholar

[12]

K. Powbunthorn, W. Abudullakasim, and J. Unartngam. Assessment of the severity of brown leaf spot disease in cassava using image analysis. In The International Conference of the Thai Society of Agricultural Engineering, Chiangmai, Thailand, 2012.

Google Scholar

Cited By

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Mathur GPandey H(2024)Precision Farming With Automated Weed Detection Using Machine LearningApplying Remote Sensing and GIS for Spatial Analysis and Decision-Making10.4018/979-8-3693-6452-9.ch009(267-310)Online publication date: 1-Nov-2024
https://doi.org/10.4018/979-8-3693-6452-9.ch009
Gapon NZhdanova MDvornichenko ARudoy DVoronin VSafaryan O(2024)Method for recognizing grape disease in bushes from images using neural network technologiesOptoelectronic Imaging and Multimedia Technology XI10.1117/12.3038925(72)Online publication date: 22-Nov-2024
https://doi.org/10.1117/12.3038925
Kumar DKukreja V(2024)Image segmentation, classification, and recognition methods for wheat diseasesComputers and Electronics in Agriculture10.1016/j.compag.2024.109005221:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109005
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Index Terms

An Integrated Digital Image Analysis System for Detection, Recognition and Diagnosis of Disease in Wheat Leaves
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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Information & Contributors

Information

Published In

WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

August 2015

763 pages

ISBN:9781450333610

DOI:10.1145/2791405

Editor:
Indu Nair
SCMS, Kochi, India
,
General Chairs:
Sushmita Mitra
Indian Statistical Institute, Kolkata, India
,
Ljiljana Trajković
Simon Fraser University, Canada
,
Program Chairs:
Punam Bedi
University of Delhi, India
,
Suzanne McIntosh
New York University and Cloudera Inc., USA
,
M. S. Rajasree
IIITM-K, Trivandrum, India

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

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Conference

WCI '15

WCI '15: Third International Symposium on Women in Computing and Informatics

August 10 - 13, 2015

Kochi, India

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WCI '15 Paper Acceptance Rate 98 of 452 submissions, 22%;

Overall Acceptance Rate 98 of 452 submissions, 22%

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Cited By

View all

Mathur GPandey H(2024)Precision Farming With Automated Weed Detection Using Machine LearningApplying Remote Sensing and GIS for Spatial Analysis and Decision-Making10.4018/979-8-3693-6452-9.ch009(267-310)Online publication date: 1-Nov-2024
https://doi.org/10.4018/979-8-3693-6452-9.ch009
Gapon NZhdanova MDvornichenko ARudoy DVoronin VSafaryan O(2024)Method for recognizing grape disease in bushes from images using neural network technologiesOptoelectronic Imaging and Multimedia Technology XI10.1117/12.3038925(72)Online publication date: 22-Nov-2024
https://doi.org/10.1117/12.3038925
Kumar DKukreja V(2024)Image segmentation, classification, and recognition methods for wheat diseasesComputers and Electronics in Agriculture10.1016/j.compag.2024.109005221:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109005
Gao CGuo WYang CGong ZYue JFu YFeng H(2024)A fast and lightweight detection model for wheat fusarium head blight spikes in natural environmentsComputers and Electronics in Agriculture10.1016/j.compag.2023.108484216(108484)Online publication date: Jan-2024
https://doi.org/10.1016/j.compag.2023.108484
Kumar DKukreja VSingh A(2024)A novel hybrid segmentation technique for identification of wheat rust diseasesMultimedia Tools and Applications10.1007/s11042-024-18463-x83:29(72221-72251)Online publication date: 9-Feb-2024
https://doi.org/10.1007/s11042-024-18463-x
Singh SYadav P(2024)A Critical Analysis Toward Sustainable Farming: The Role of AI in Weed Identification and ManagementCommunication and Intelligent Systems10.1007/978-981-97-2053-8_28(369-382)Online publication date: 21-Jun-2024
https://doi.org/10.1007/978-981-97-2053-8_28
Sakshi Rudrawar Yogini Pujari Apeksha Savant Prof. Mrutyunjay Kumar (2023)Survey Paper: Plant Disease Detection using CNNInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-9031(220-223)Online publication date: 6-Apr-2023
https://doi.org/10.48175/IJARSCT-9031
Upadhyaya MNagendrappa Malvade NKumar Shukla AWalia RDevi K(2023)Design and Comparison Of Deep Learning Architecture For Image-based Detection of Plant DiseasesAI and IoT-based Intelligent Health Care & Sanitation10.2174/9789815136531123010017(222-239)Online publication date: 10-Apr-2023
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Chanyal HYadav R(2023)Machine Learning-Based Plant Disease Detection for Agricultural Applications: A Review2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon)10.1109/SmartTechCon57526.2023.10391776(657-661)Online publication date: 18-Aug-2023
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Kumar SSingh RKumar SGupta S(2023)Light Weight ResNet for Detection of Wheat Yellow Rust over Mobile Captured Images from Wheat Fields2023 3rd Asian Conference on Innovation in Technology (ASIANCON)10.1109/ASIANCON58793.2023.10270562(1-4)Online publication date: 25-Aug-2023
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