Plant Disease Detection Using Digital Image Processing: Opportunities and Challenges

Leni Fitriani; Dini Destiani Siti Fatimah; Dewi Tresnawati

doi:10.15575/join.v10i2.1330

Authors

Leni Fitriani Department of Computer Science, Institut Teknologi Garut, Indonesia https://orcid.org/0000-0002-9035-5862
Dini Destiani Siti Fatimah Department of Computer Science, Institut Teknologi Garut, Indonesia
Dewi Tresnawati Department of Computer Science, Institut Teknologi Garut, Indonesia

DOI:

https://doi.org/10.15575/join.v10i2.1330

Keywords:

Detection, Digital, Image, Plant, Processing

Abstract

Diseases in plants affect the yield of the plant itself. Agriculture is essential in human life, and if plant conditions are left unchecked, it will result in crop failure, which can affect the economy. Many researchers have developed methods to detect plant diseases, ranging from expert systems to deep learning algorithms. Machine learning is particularly effective for this task as it relies on datasets composed of plant images, making image processing crucial for the identification process. This article reviews the current literature and identifies several research gaps, opportunities, and challenges that must be addressed. Specifically, the article outlines potential avenues for future research in detecting plant diseases using image processing techniques. A significant opportunity exists to develop more effective algorithmic models for detecting plant diseases.

Author Biography

Leni Fitriani, Department of Computer Science, Institut Teknologi Garut

Department of Informatics, Lektor

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