Rice Plant Disease Detection Using Sensing Recognition Strategy Based on Artificial Intelligence

Authors

  • T. Daniya Department of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh, India https://orcid.org/0000-0001-7549-7419
  • Ch. Vidyadhari Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India
  • Srilakshmi Aluri Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India

DOI:

https://doi.org/10.13052/jmm1550-4646.18311

Keywords:

Rice Plant Disease, Grasshopper, Sensing Recognition Strategy, Artificial Intelligence

Abstract

In current history rice infections have often appeared, causing severe destruction of rice cultivation. As one of the top ten countries that creates and destroys the world, India relies heavily on rice for its economy and to meet its food needs. To ensure the sound and legal growth of rice crops it is important to identify any diseases in the schedule and to pre-apply the expected treatment to the affected plants. Since the detection of disease is time-consuming and labor-intensive, it is certainly wise to have a system with robots. Infection of rice crops is considered to be a growing factor behind the horticultural, financial and general situation in the future development of the rural field. However, leaf scald and eyespot are the pivotal trouble in paddy fields. Hence, to conquer these issues a novel Sensing Recognition Strategy has been proposed. In Proposed method, optical sensors identify identification of disease and Enhanced Grasshopper Detection Algorithm utilizing the grasshoppers’ forces, path and position carries out detection. The accuracy of the suggested framework is to attain 97.94% with healthy rice crops.

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Author Biographies

T. Daniya, Department of Information Technology, GMR Institute of Technology, Rajam, Andhra Pradesh, India

T. Daniya received her B.Tech degree in Information Technology from Anna University, Chennai, India and M.Tech degree from MS University, India, in 2009 and 2011 respectively. She is currently doing Ph.D degree in Computer Science and Engineering at Sathyabama Institute of Science and Technology Chennai, India. She is currently working as an Assistant Professor with the Department of Information Technology, GMRIT, Rajam, India. Her research interest is AI, Machine Learning and Deep Learning. She Published 15 research articles in various journals and conferences.

Ch. Vidyadhari, Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India

Ch. Vidyadhari currently working as an Assistant professor in the Department of Information Technology at Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Kukatpally, Hyderabad. Her research area is Data Mining and Machine Learning. She has published around 10 papers in reputed international journals and has an experience of 12 years in teaching.

Srilakshmi Aluri, Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India

Srilakshmi Aluri working as an Assistant Professor, Department of Information Technology, in Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, Telangana, India. She completed M.Tech in Computer Science and Engineering from JNTU Kakinada in the year 2013. Her area of research interest includes Artificial Intelligence, Machine Learning, and Data Science. She has 8 years of teaching experience.

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Published

2022-01-22

How to Cite

Daniya, T. ., Vidyadhari, C. ., & Aluri, S. . (2022). Rice Plant Disease Detection Using Sensing Recognition Strategy Based on Artificial Intelligence: . Journal of Mobile Multimedia, 18(03), 705–722. https://doi.org/10.13052/jmm1550-4646.18311

Issue

Section

Computer Vision and its Application in Agriculture