AUTOMATED BIRD SCARING SYSTEM: A REVIEW

Authors

  • F.O. DURODOLA Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta.
  • S.I. ELUDIORA Department of Computer Science and Engineering, Obafemi Awolowo University, Ile Ife, Nigeria
  • S.O. OWOEYE Department of Agricultural and Bio-resources Engineering, Federal University of Agriculture, Abeokuta
  • P.O. OMOTAINSE Department of Agricultural and Bio-resources Engineering, Federal University of Agriculture, Abeokuta
  • O.O .ODUNTAN Department of Forestry and Wildlife Management, Federal University of Agriculture, Abeokuta
  • B.B. ALAKE Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta

Abstract

Birds are a considerable menace to crop farming, resulting in major economic detriment through crop damage. Conventional avian deterrence techniques, including scarecrows, reflecting substances, and chemical repellents, have demonstrated ineffectiveness over time due to habituation. This research examines diverse ways for bird detection and deterrent, classifying them into singular and multimodal approaches. Contemporary innovations, like as artificial intelligence (AI), drones, machine vision, and deep learning-based detection, have markedly enhanced the efficacy of avian management systems. The review emphasises the advantages of combining detection with adaptive deterrence strategies, including Unmanned Aerial Vehicle (UAV)-based acoustic deterrents, AI-driven repellent systems, and laser-based frightening techniques. Moreover, multimodal deterrence—integrating visual, aural, and physical barriers—demonstrates the most efficacy in mitigating avian-related damage. The paper indicates that subsequent research should prioritise the development of cost-efficient, automated, and species-specific deterrent methods that reduce habituation and improve sustainability in crop protection.

 

Author Biographies

F.O. DURODOLA, Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta.

Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta.

 

S.I. ELUDIORA, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile Ife, Nigeria

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile Ife, Nigeria

S.O. OWOEYE, Department of Agricultural and Bio-resources Engineering, Federal University of Agriculture, Abeokuta

Department of Agricultural and Bio-resources Engineering, Federal University of

Agriculture, Abeokuta

P.O. OMOTAINSE, Department of Agricultural and Bio-resources Engineering, Federal University of Agriculture, Abeokuta

Department of Agricultural and Bio-resources Engineering, Federal University of

Agriculture, Abeokuta

O.O .ODUNTAN, Department of Forestry and Wildlife Management, Federal University of Agriculture, Abeokuta

Department of Forestry and Wildlife Management, Federal University of Agriculture,

Abeokuta

B.B. ALAKE, Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta

Department of Mechatronics Engineering, Federal University of Agriculture, Abeokuta

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Published

2025-07-11

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