Region Based Segmentation with Enhanced Adaptive Histogram Equalization Model with Definite Feature Set for Sugarcane Leaf Disease Classification

Authors

  • A. Vivek Reddy Research Scholar, Department of CSE,Annamalai University Annamalai Nagar, Tamilnadu, 608002, India
  • R. Thiruvengatanadhan Department of CSE, Annamalai University Annamalai Nagar, Tamilnadu, 608002, India.
  • M. Srinivas Department of CSE, St.mary’s Group Of Institutions - Hyderabad, Deshmukhi, Telangana,508284, India
  • P. Dhanalakshmi Department of CSE, Annamalai University Annamalai Nagar, Tamilnadu, 608002, India

Keywords:

Sugarcane Leaf, Image Processing, Segmentation, Histogram Equalization, Leaf Features, Feature Set, Classification, Disease Detection, Quality Enhancemen

Abstract

Visual identification of plant diseases is a time-consuming process that yields inaccurate results and is only feasible in small settings. Instead, an autonomous detection method would require less time and manpower while also improving accuracy. Brown and yellow spots, late and early scorch, and other fungal, viral, and bacterial diseases are only a few of the more common plant ailments. Manually detecting the disease as well as the type of disease requires analyzing the color degradation in a diseased leaf or plant. This research will automate the human-performed step of disease identification and instill the methods by which humans recognize diseases from healthy plants. The proposed model after enhancing the image quality, features is extracted and relevant features are selected. The proposed model uses Enhanced K Nearest Neighbor (EKNN) model for accurate classification of disease and non disease leaves. In this research, Region based Segmentation with Enhanced Adaptive Histogram Equalization based Image Quality Enhancement model with Definite Feature Set model using EKNN (RbS-EAHE-EKNN-LDC) for Leaf Disease Classification is proposed for considering the sugarcane images and enhancing the image quality to perform accurate feature extraction for accurate disease or non disease classification. The proposed model is contrasted with the state of the art models and the results represent that the proposed model performance is enhanced.

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Published

02.09.2023

How to Cite

Reddy, A. V. ., Thiruvengatanadhan, R. ., Srinivas, M. ., & Dhanalakshmi, P. . (2023). Region Based Segmentation with Enhanced Adaptive Histogram Equalization Model with Definite Feature Set for Sugarcane Leaf Disease Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 428–442. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3426

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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