Design an Ant Lion-Based Yolo-V5 Model for Prediction and Classification of Paddy Leaf Disease

Authors

  • Nilamadhab Mishra Professor, School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India-466114
  • J. Seetha Associate professor Department of Computer Science and Business Systems Panimalar Engineering College Chennai
  • Arra Ganga Dinesh Kumar SMIEEE Professor Malla Reddy Enginnering College for Women
  • Supriya Menon M. Assistant Professor, Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522501, AP, India.
  • Ananda Ravuri Senior Software Engineer Intel corporation Hillsboro, Oregon 97124 USA

Keywords:

Paddy Leaf Disease Detection, Grey-Level Co-Occurrence Matrix, Ant Lion Optimization, Principal Component Analysis, GrabCut, Segmentation, Deep Learning

Abstract

It is crucial to identify crop diseases early to educate farmers on how to stop the spread of diseases in their crops. However, the agriculture sector's output is impacted by the emergence of numerous crop-related diseases. Multiple methods for predicting paddy leaf diseases have been created, but they still suffer from overfitting, poor detection, and classification issues. To overcome these issues, design a novel Ant Lion-based YOLO-V5 (AL-YOLOv5) system to improve the system's functionality to detect paddy leaf disease. Paddy leaf photos were initially gathered from the internet and trained in the system. Brown spot, Leaf blast, Healthy, and Hispa are the four paddy leaf diseases the proposed model intends to classify better and identify. The dataset's noise is removed during the preprocessing stage, and the GrabCut algorithm is used to segment the impacted areas based on the pixels. The Grey-Level Co-Occurrence Matrix (GLCM), which extracts form, texture, and color features, is also used for feature extraction. Finally, utilize a YOLOv5 network to find and categorize the crop's affected diseases. The created model uses ant lion fitness to forecast paddy leaf diseases correctly. By achieving improved performance metrics, the experimental findings demonstrate the effectiveness of the designed model, and the obtained results are validated with other traditional models in terms of accuracy, precision, recall, F-score, and error rate.

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Architecture of the proposed methodology

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Published

17.05.2023

How to Cite

Mishra, N. ., Seetha, J. ., Dinesh Kumar, A. G. ., Menon M., S. ., & Ravuri, A. . (2023). Design an Ant Lion-Based Yolo-V5 Model for Prediction and Classification of Paddy Leaf Disease. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 599–612. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2892

Issue

Vol. 11 No. 6s (2023)

Section

Research Article

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