ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Identification and recognition of Leaf Disease Using Enhanced Segmentation Techniques


Affiliations
1 Department of Information Technology, Siddhant College of Engineering, India
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, India
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, India
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, India
     

   Subscribe/Renew Journal


Segmenting refers to the technique of breaking up an image into its component parts one by one. When it comes to the process of segmenting photos, there is a plethora of choice available at current point in time. These options range from the easy thresholding approach to the complicated color image segmentation techniques. The bulk of the time, the parts that go into making up these sub-assemblies are items that individuals are able to easily identify and categorize as being distinct from one another. As a result of the limitation of computer lack of intelligence to differentiate between distinct items, a wide variety of techniques have been devised and utilized in the process of segmenting photographs. In order to complete its tasks, the image segmentation algorithm requires a wide range of image characteristics to be provided as input. This could be referring to the colors that are contained within an image, the borders that are included within the image, or a particular region that is contained within the image. In order to break down color images into their component elements, we make use of an algorithm that is inspired by natural selection. The research uses enhanced segmentation techniques to identify and recognize the leaf disease in plants. The study conducts extensive simulation to test the efficacy of the model. The results show that the proposed method achieves higher segmentation accuracy than other methods.

Keywords

No Keywords.
Subscription Login to verify subscription
User
Notifications
Font Size

  • R.K. Nayak, R. Tripathy and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of 4th International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.

  • B. Subramanian, V. Saravanan and S. Hariprasath, “Diabetic Retinopathy-Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal of Engineering and Advanced Technology, Vol. 9, pp. 618-627, 2019.

  • R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-8, 2022.

  • V. Pooja and V. Kanchana, “Identification of Plant Leaf Diseases using Image Processing Techniques”, Proceedings of IEEE Technological Innovations in ICT for Agriculture and Rural Development, pp. 130-133, 2017.

  • N. Krithika and A.G. Selvarani, “An Individual Grape Leaf Disease Identification using Leaf Skeletons and KNN Classification”, Proceedings of International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-5, 2017.

  • P. Kantale and S. Thakare, “A Review on Pomegranate Disease Classification using Machine Learning and Image Segmentation Techniques”, Proceedings of International Conference on Intelligent Computing and Control Systems, pp. 455-460, 2020.

  • T. Fang and B. Wang, “Crop Leaf Disease Grade Identification based on an Improved Convolutional Neural Network”, Journal of Electronic Imaging, Vol. 29, No. 1, pp. 1-14, 2020.

  • P. Kaur, S. Bhatia and A.M. Alabdali, “Recognition of Leaf Disease using Hybrid Convolutional Neural Network by Applying Feature Reduction”, Sensors, Vol. 22, No. 2, pp. 575-583, 2022.

  • S. Bashir and N. Sharma, “Remote Area Plant Disease Detection using Image Processing”, IOSR Journal of Electronics and Communication Engineering, Vol. 2, No. 6, pp. 31-34, 2012.

  • V. Singh and A.K. Misra, “Detection of Unhealthy Region of Plant Leaves using Image Processing and Genetic Algorithm”, Proceedings of International Conference on Advances in Computer Engineering and Applications, pp. 1028-1032, 2015.

  • P. Revathi and M. Hemalatha, “Classification of Cotton Leaf Spot Diseases using Image Processing Edge Detection Techniques”, Proceedings of International Conference on Emerging Trends in Science, Engineering and Technology, pp. 169-173, 2012.

  • A.S. Tulshan and N. Raul, “Plant Leaf Disease Detection using Machine Learning”, Proceedings of International Conference on Computing, Communication and Networking Technologies, pp. 1-6, 2019.


Abstract Views: 90

PDF Views: 1




  • Identification and recognition of Leaf Disease Using Enhanced Segmentation Techniques

Abstract Views: 90  |  PDF Views: 1

Authors

Brijendra Gupta
Department of Information Technology, Siddhant College of Engineering, India
V. Elanangai
Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, India
G. N. Naveen Kumar
Department of Electronics and Communication Engineering, CMR Institute of Technology, India
P. T. Kalaivaani
Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, India

Abstract


Segmenting refers to the technique of breaking up an image into its component parts one by one. When it comes to the process of segmenting photos, there is a plethora of choice available at current point in time. These options range from the easy thresholding approach to the complicated color image segmentation techniques. The bulk of the time, the parts that go into making up these sub-assemblies are items that individuals are able to easily identify and categorize as being distinct from one another. As a result of the limitation of computer lack of intelligence to differentiate between distinct items, a wide variety of techniques have been devised and utilized in the process of segmenting photographs. In order to complete its tasks, the image segmentation algorithm requires a wide range of image characteristics to be provided as input. This could be referring to the colors that are contained within an image, the borders that are included within the image, or a particular region that is contained within the image. In order to break down color images into their component elements, we make use of an algorithm that is inspired by natural selection. The research uses enhanced segmentation techniques to identify and recognize the leaf disease in plants. The study conducts extensive simulation to test the efficacy of the model. The results show that the proposed method achieves higher segmentation accuracy than other methods.

Keywords


No Keywords.

References