Plant Disease Detection Using Computational Approaches: A Systematic Literature Review

Arslan Akram ()
Additional contact information
Arslan Akram: University of Central Punjab, Lahore, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 2014-2026

Abstract: Rapid improvements in ML and DL techniques have made it possible to detect and recognize objects from images. Computational approaches using ML and DL have been recently applied toagriculture or farming applications and are proving successful in increasing per-yield production. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use DL and ML approaches to address various plant DDconcerns was undertaken in the current publication. In this work, we have reviewed 35 of the most recent DL and ML-based articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the ML and DL used in plant leaf DD. Moreover, DCNNtrained on image data wasthe most effective method for detecting early DD. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant DD.

Keywords: ML; DD; DL; IP (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/1131/1671 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/1131 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:4:p:2014-2026

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().