Authors :
Abdullahi Lawal Rukuna; Dr. F. U. Zambuk; Dr. A. Y. Gital; Umar Muhammad Bello; Kaje Danladi Shemang; Nahuru Ado Sabongari
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/bdz68p84
Scribd :
https://tinyurl.com/2c2shjwv
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1459
Abstract :
Citrus diseases pose significant threats to
global agriculture, impacting crop yield and quality. In
recent years the integration of deep learning models has
surfaced as a hopeful method for classifying and
detecting diseases. This review critically analyzes and
synthesizes 25 research works that explore various deep
learning models applications in citrus disease detection
and classification. The methodology involves a
systematic literature search, filtering based on relevance,
publication date, and language. The selected works are
categorized, and each is analyzed for contributions and
limitations. The review identifies limitations, notably the
reliance on limited datasets leading to issues of
generalization and class imbalance. Data augmentation,
while employed, lacks comprehensive evaluation.
Practical implementation in real-world agricultural
settings remains a challenge, demanding scalable,
adaptable, and robust solutions. Future research
directions are proposed to address limitations. Emphasis
is placed on curating larger and diverse datasets, actively
mitigating class imbalance, and rigorously evaluating
data augmentation techniques.
Keywords :
Citrus Diseases, Deep Learning, Disease Detection, Disease Classification.
Citrus diseases pose significant threats to
global agriculture, impacting crop yield and quality. In
recent years the integration of deep learning models has
surfaced as a hopeful method for classifying and
detecting diseases. This review critically analyzes and
synthesizes 25 research works that explore various deep
learning models applications in citrus disease detection
and classification. The methodology involves a
systematic literature search, filtering based on relevance,
publication date, and language. The selected works are
categorized, and each is analyzed for contributions and
limitations. The review identifies limitations, notably the
reliance on limited datasets leading to issues of
generalization and class imbalance. Data augmentation,
while employed, lacks comprehensive evaluation.
Practical implementation in real-world agricultural
settings remains a challenge, demanding scalable,
adaptable, and robust solutions. Future research
directions are proposed to address limitations. Emphasis
is placed on curating larger and diverse datasets, actively
mitigating class imbalance, and rigorously evaluating
data augmentation techniques.
Keywords :
Citrus Diseases, Deep Learning, Disease Detection, Disease Classification.
