An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild
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Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
One of the biggest challenges that the farmers go through is to fight insect pests during
agricultural product yields. The problem can be solved easily and avoid economic
losses by taking timely preventive measures. This requires identifying insect pests in
an easy and effective manner. Most of the insect species have similarities between
them. Without proper help from the agriculturist academician it’s very challenging for
the farmers to identify the crop pests accurately. To address this issue we have done
extensive experiments considering different methods to find out the best method among
all. This paper presents a detailed overview of the experiments done on mainly a robust
dataset named IP102 including transfer learning + finetuning, attention mechanism
and custom architecture. Some example from another dataset D0 is also shown to
show robustness of our experimented techniques. In both datasets our proposed model
performed very well with an accuracy of 78% and 99.70% respectively.
Description
Supervised by
Prof. Dr. Md. Hasanul Kabir,
Co-Supervisor,
Mr. Sabbir Ahmed,
Assistant Professor,
Department of Computer Science and Engineering(CSE),
Islamic University of Technology(IUT),
Board Bazar, Gazipur-1704, Bangladesh
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Citation
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