https://doi.org/10.4081/jae.2023.1532
Parametric evaluation of segmentation techniques for paddy diseases analysis
Published: 4 August 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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In most paddy plant diseases, the leaf is the primary source of information for image-based disease identification and classification. Image segmentation is an important step in the plant disease analysis process. It is used to separate the normal part of the leaf from the disease-affected part of the leaf. In this paper diseases like Bacterial leaf blight (BLB), Brown spot (BS), and Leaf smut (LS) are segmented using existing, K-means clustering, the Otsu thresholding method. Color space-based segmentation is newly proposed for paddy disease analysis. The intelligence of segmentation techniques is evaluated using the Error Rate (ER) and Overlap Rate (OR) across the three paddy diseases namely, BLB, BS, and LS. The results were compared among the Otsu, K-means and color thresholding segmentation techniques. The results revealed that the color thresholding method using the Lab model emerged as a novel segmentation method for all three paddy diseases with an average ER and OR of [0.36, 0.95]. The proposed work is carried out in the Department of Electronics and Communication research center at Ballari Institute of Technology and Management, Ballari, Karnataka during the period from August 2022 to February 2023 with the expert suggestions of the plant pathologist, from the University of Agricultural Science, Dharwad, Karnataka.
Anthonys, G., and N. Wickramarachchi. 2009. “An Image Recognition System for Crop Disease Identification of Paddy Fields in Sri Lanka.” In 2009 International Conference on Industrial and Information Systems (ICIIS), 403–7. https://doi.org/10.1109/ICIINFS.2009.5429828.
DOI: https://doi.org/10.1109/ICIINFS.2009.5429828
Barbedo, Jayme Garcia Arnal. 2016. “A Review on the Main Challenges in Automatic Plant Disease Identification Based on Visible Range Images.” Biosystems Engineering 144 (April): 52–60. https://doi.org/10.1016/j.biosystemseng.2016.01.017.
DOI: https://doi.org/10.1016/j.biosystemseng.2016.01.017
Devi, D.Amutha, and K. Muthukannan. 2014. “Analysis of Segmentation Scheme for Diseased Rice Leaves.” In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, 1374–78. https://doi.org/10.1109/ICACCCT.2014.7019325.
DOI: https://doi.org/10.1109/ICACCCT.2014.7019325
Dhingra, Gittaly, Vinay Kumar, and Hem Dutt Joshi. 2018. “Study of Digital Image Processing Techniques for Leaf Disease Detection and Classification.” Multimedia Tools and Applications 77 (15): 19951–0. https://doi.org/10.1007/s11042-017-5445-8.
DOI: https://doi.org/10.1007/s11042-017-5445-8
Gayathri Devi, T., and P. Neelamegam. 2019. “Image Processing Based Rice Plant Leaves Diseases in Thanjavur, Tamilnadu.” Cluster Computing 22 (6): 13415–28. https://doi.org/10.1007/s10586-018-1949-x.
DOI: https://doi.org/10.1007/s10586-018-1949-x
Guru, D. S., P. B. Mallikarjuna, and S. Manjunath. 2011. “Segmentation and Classification of Tobacco Seedling Diseases.” In Proceedings of the Fourth Annual ACM Bangalore Conference, 1–5. COMPUTE ’11. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/1980422.1980454.
DOI: https://doi.org/10.1145/1980422.1980454
Kappali, Hemanthakumar R., K.M. Sadyojatha, and S.K. Prashanthi. 2023. “Computer Vision and Machine Learning in Paddy Diseases Identification and Classification: A Review.” Indian Journal Of Agricultural Research, no. Of (March). https://doi.org/10.18805/IJARe.A-6061.
DOI: https://doi.org/10.18805/IJARe.A-6061
Khattab, Dina, Hala Mousher Ebied, Ashraf Saad Hussein, and Mohamed Fahmy Tolba. 2014. “Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut.” The Scientific World Journal 2014 (August): e126025. https://doi.org/10.1155/2014/126025.
DOI: https://doi.org/10.1155/2014/126025
Khirade, Sachin D., and A.B. Patil. 2015. “Plant Disease Detection Using Image Processing.” In 2015 International Conference on Computing Communication Control and Automation, 768–71. https://doi.org/10.1109/ICCUBEA.2015.153.
DOI: https://doi.org/10.1109/ICCUBEA.2015.153
Kurniawati, Nunik Noviana, Siti Norul Huda Sheikh Abdullah, Salwani Abdullah, and Saad Abdullah. 2009. “Investigation on Image Processing Techniques for Diagnosing Paddy Diseases.” In 2009 International Conference of Soft Computing and Pattern Recognition, 272–77. https://doi.org/10.1109/SoCPaR.2009.62.
DOI: https://doi.org/10.1109/SoCPaR.2009.62
Mai, Xiaochun, and Max Q.-H. Meng. 2016. “Automatic Lesion Segmentation from Rice Leaf Blast Field Images Based on Random Forest.” In 2016 IEEE International Conference on Real-Time Computing and Robotics (RCAR), 255–59. https://doi.org/10.1109/RCAR.2016.7784035.
DOI: https://doi.org/10.1109/RCAR.2016.7784035
Narmadha, R. P., and G. Arulvadivu. 2017. “Detection and Measurement of Paddy Leaf Disease Symptoms Using Image Processing.” In 2017 International Conference on Computer Communication and Informatics (ICCCI), 1–4. https://doi.org/10.1109/ICCCI.2017.8117730.
DOI: https://doi.org/10.1109/ICCCI.2017.8117730
Orillo, John William, Jennifer Dela Cruz, Leobelle Agapito, Paul Jensen Satimbre, and Ira Valenzuela. 2014. “Identification of Diseases in Rice Plant (Oryza Sativa) Using Back Propagation Artificial Neural Network.” In 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 1–6. https://doi.org/10.1109/HNICEM.2014.7016248.
DOI: https://doi.org/10.1109/HNICEM.2014.7016248
Phadikar, Santanu, and Jyotirmoy Goswami. 2016. “Vegetation Indices Based Segmentation for Automatic Classification of Brown Spot and Blast Diseases of Rice.” In 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), 284–89. https://doi.org/10.1109/RAIT.2016.7507917.
DOI: https://doi.org/10.1109/RAIT.2016.7507917
Pinki, Farhana Tazmim, Nipa Khatun, and S.M. Mohidul Islam. 2017. “Content Based Paddy Leaf Disease Recognition and Remedy Prediction Using Support Vector Machine.” In 2017 20th International Conference of Computer and Information Technology (ICCIT), 1–5. https://doi.org/10.1109/ICCITECHN.2017.8281764.
DOI: https://doi.org/10.1109/ICCITECHN.2017.8281764
Qiangqiang, Zhou, Wang Zhicheng, Zhao Weidong, and Chen Yufei. 2015. “Contour-Based Plant Leaf Image Segmentation Using Visual Saliency.” In Image and Graphics, edited by Yu-Jin Zhang, 48–59. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-21963-9_5.
DOI: https://doi.org/10.1007/978-3-319-21963-9_5
Ramesh, S., and D. Vydeki. 2020. “Rice Disease Detection and Classification Using Deep Neural Network Algorithm.” In Micro-Electronics and Telecommunication Engineering, edited by Devendra Kumar Sharma, Valentina Emilia Balas, Le Hoang Son, Rohit Sharma, and Korhan Cengiz, 555–66. Lecture Notes in Networks and Systems. Singapore: Springer. https://doi.org/10.1007/978-981-15-2329-8_56.
DOI: https://doi.org/10.1007/978-981-15-2329-8_56
Singh, Jaskaran, and Harpreet Kaur. 2018. “A Review on: Various Techniques of Plant Leaf Disease Detection.” In 2018 2nd International Conference on Inventive Systems and Control (ICISC), 232–38. https://doi.org/10.1109/ICISC.2018.8399069.
DOI: https://doi.org/10.1109/ICISC.2018.8399069
Zhang, Jun., Lifei. Yan, and Jinyi. Hou. 2018. “Recognition of Rice Leaf Diseases Based on Salient Characteristics.” In 2018 13th World Congress on Intelligent Control and Automation (WCICA), 801–6. https://doi.org/10.1109/WCICA.2018.8630414.
DOI: https://doi.org/10.1109/WCICA.2018.8630414
How to Cite
Kappali, H. R., K.M., S. . and S.K., P. . (2023) “Parametric evaluation of segmentation techniques for paddy diseases analysis”, Journal of Agricultural Engineering, 54(4). doi: 10.4081/jae.2023.1532.
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