IEEE Consumer Electronics Magazine - May/June 2022 - 49

datasets. Furthermore, data augmentation and
transfer learning techniques are adopted to
overcome the lack of a manually annotated dataset.
An added benefit of these techniques helps
the proposed model to improve detection performance
in less time. Empirical results demonstrate
that the proposed model gives satisfiable
results in identifying small infected portions on
the diseased leaves. Furthermore, compared
with other recent state-of-art models, the proposed
model gives a good mAP of 74.10%. In the
future, we intend to focus on the attention mechanism
of EfficientDet by extending the dataset to
include more disease crop species making as a
practical tool for optimal disease detection for
real-time surveillance in open-field-large-scale
cultivations. Future work will be intended to
focus on extending the dataset to include more
disease crop species making as a practical tool
for optimal disease detection for real-time surveillance
in open field large-scale cultivations.
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R. Kavitha Lakshmi is currently working toward
the Ph.D. degree with the Department of Computer
Applications, National Institute of Technology Tiruchirappalli,
Tiruchirappalli, India. She is the corresponding
author or this article. Contact her at
kavitha.rachabattuni@gmail.com.
Nickolas Savarimuthu is currently a Professor
with the Department of Computer Applications,
National Institute of Technology Tiruchirappalli, Tiruchirappalli,
India. Contact him at nickolas@nitt.edu.
May/June 2022
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