ARTIFICIAL INTELLIGENCE DRIVEN DRONE OBSERVATION AND PEST CONTROL IN BANANA CROP: A SYSTEMATIC REVIEW

Authors

  • Shahzad Nasim Ziauddin University, Faculty of Engineering, Science, Technology & Management. ZUFESTM Author
  • Munaf Rashid Ziauddin University, Faculty of Engineering, Science, Technology & Management. ZUFESTM Author
  • Saba Yousha Department of Information and Communication Technologies, The Begum Nusrat Bhutto Women University Sukkur, Sindh, Pakistan Author

DOI:

https://doi.org/10.71146/kjmr197

Keywords:

Banana, Controlled Adaptive, Pest, Image Classification

Abstract

Bananas are the most commonly eaten and significant fruit in global trade. Bananas are produced using a variety of methods and environments. In addition to regularly updating farmers on problems in banana plant leaves, this system aims to discover, diagnose, and treat banana leaf diseases. Customers' wants and lifestyles have changed significantly during the past few decades. These modifications provide additional difficulties for farmers whose output must satisfy consumer needs. Both the farmer and the consumer will benefit from the capacity to categorize agricultural products according to size and quality. In this case, the system gets its input in the form of standard photos of banana leaves taken using various image capture devices. It will then process those photos to identify any diseases and alert the farmer. Additionally, the system will advise the farmer on what to do next, including which fertilizers, herbicides, and agricultural practices to employ in order to prevent illnesses from harming neighboring crops. In this systematic review, useful and efficient methods for identification are presented in works that fall under the categories of image classification, AI/ML, deep learning, and mobile applications.

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Published

2025-01-14

Issue

Section

Natural Sciences

How to Cite

ARTIFICIAL INTELLIGENCE DRIVEN DRONE OBSERVATION AND PEST CONTROL IN BANANA CROP: A SYSTEMATIC REVIEW. (2025). Kashf Journal of Multidisciplinary Research, 2(01), 16-36. https://doi.org/10.71146/kjmr197

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