ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE PEST DETECTION IN BANANA FIELD: A SYSTEMATIC REVIEW

Shahzad Nasim; Munaf Rashid; Sidra Abid Syed; Imtiaz  Brohi

doi:10.34016/pjbt.2023.20.02.746

Authors

Shahzad Nasim Begum Nusrat Bhutto Women University, Faculty of Management Information Science and Technology, Sukkur, Pakistan
Munaf Rashid Ziauddin University, Department of Computer Science and Software Engineering , Karachi, Pakistan
Sidra Abid Syed Sir Syed University of Engineering and Technology, Department of Biomedical Engineering
Imtiaz Brohi Begum Nusrat Bhutto Women University, Department of Information & Communication Technologies, Sukkur,

DOI:

https://doi.org/10.34016/pjbt.2023.20.02.746

Keywords:

pest, banana, detection, Plant, disease

Abstract

Purpose: This systematic review details the diseases that influence banana production and their detection. A common method for identifying plant diseases in plants is image processing. Segmentation is one method for using image processing to establish medical diagnosis. The main objective of this study is to identify, categorize, and evaluate several image processing techniques used to control pests in a banana crop.

Methodology: An electronic search was conducted using relevant keywords on openly available databases including IEEE Xplore, PubMed, Science Direct, and Google Scholar. 104 items were discovered by the search engine. After removing the duplicates, there were 56 research papers remained, but 22 of them were discarded after title and abstract checks since they did not address insect detection in banana fields.

Results: 22 papers that come under the headings of image classification, AI/ML, deep learning, and mobile applications provide usable and reliable detection techniques in this systematic review

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