Research Article Open Access

Applications of Artificial Intelligence Based Technologies in Weed and Pest Detection

Nidhi Gupta1,2, Bharat Gupta2, Kalpdrum Passi3 and Chakresh Kumar Jain4
  • 1 Department of Information Technology, Ajay Kumar Garg Engineering College, Ghaziabad, India
  • 2 Department of Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India
  • 3 School of Engineering and Computer Science, Laurentian University, Sudbury, Canada
  • 4 Department of Biotechnology, Jaypee Institute of Information Technology, Noida, India

Abstract

Unprecedented population growth and climate change has burdened food security and scarcity worldwide, where the agriculture sector can significantly contribute to accomplishing the demands and contribute to the economic growth of a country. Artificial Intelligence (AI) has revolutionized the agricultural domain. Pest and weed detection is significant to yielding good quality crops. The AI-based tools and technologies such as drones and robots bring advancement in crop production by performing the early detection of weeds and pests. The tools utilize image processing and machine learning algorithms to capture, analyze and detect the presence of weeds and pests in plants. The research work carried out provides a comprehensive survey for the application of artificial intelligence for both weed and pest detection. It presents widely used techniques, their evaluation parameters, and publicly available datasets which provide the current status of work for the researchers working in the domain of weed and pest detection.

Journal of Computer Science
Volume 18 No. 6, 2022, 520-529

DOI: https://doi.org/10.3844/jcssp.2022.520.529

Submitted On: 15 May 2022 Published On: 2 July 2022

How to Cite: Gupta, N., Gupta, B., Passi, K. & Jain, C. K. (2022). Applications of Artificial Intelligence Based Technologies in Weed and Pest Detection. Journal of Computer Science, 18(6), 520-529. https://doi.org/10.3844/jcssp.2022.520.529

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Keywords

  • Weed Detection
  • Pest Detection
  • Machine Learning
  • Deep Learning
  • Image Processing