PADDY LEAF DISEASE SYMPTOMS DETECTION THROUGH ARTIFICIAL NEURAL NETWORK

Authors

  • SUNDAS NAQEEB KHAN Department of Graphics Computer Vision and Digital Systems, Silesian University of Technology, Gilwice, Poland.
  • SAMRA UROOJ KHAN Department of Electrical Engineering Technology, Punjab University of Technology, Punjab, Pakistan.
  • SOHAIB AHMED Department of Information and Technology, University of the Punjab Jhelum Campus, Punjab, Pakistan.
  • MUHAMMAD AMMAR KHAN Department of Information and Technology, University of the Punjab Jhelum Campus, Punjab, Pakistan.
  • JAWAD KHAN Department of Information and Technology, University of the Punjab Jhelum Campus, Punjab, Pakistan.

Keywords:

paddy disease, feature extraction, ANN, HSV, performance, accuracy

Abstract

Changing from one disease control strategy to another presents significant challenges for farmers. It can be costly to diagnose and categorize diseases just by naked-eye observation. By shortening the life of the plants, numerous plant diseases represent a serious threat to the agricultural industry. The goal of the current work is to provide a straightforward system for paddy disease detection. Usually, the leaves, stems, or fruit are examined to identify the attack signs. The effective method for diagnosing plant diseases by inspection of leaf features is included in this suggested system. To assess the health of the rice plants, experiments are performed on the set of leaves according to the classified form. Diagnosing plant diseases requires both science and art. Inherently visual, the diagnosis process (i.e., identifying symptoms and indications) calls for both intuitive judgement and the use of scientific principles. The first step in the process is input as image. From the segmentation output, color features like hue saturation value (HSV) features are retrieved, and an ANN is then trained by selecting the feature values that can accurately distinguish between healthy and diseased samples. According to experimental findings, symptoms detection performance using ANN with accuracy is better. In the current study, a method for early and precise detection of paddy leaf diseases employing a variety of image processing techniques and ANN is the more accurate approach for this.

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Published

2023-12-17

How to Cite

KHAN, S. N., KHAN, S. U., AHMED, S., KHAN, M. A., & KHAN, J. (2023). PADDY LEAF DISEASE SYMPTOMS DETECTION THROUGH ARTIFICIAL NEURAL NETWORK. Quantum Journal of Engineering, Science and Technology, 4(4), 1–10. Retrieved from https://qjoest.com/index.php/qjoest/article/view/123

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