An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild

Mahjabin, Mohammad Ratul; Rahman, Md Sabbir; Raf, Mohtasim Hadi

dc.contributor.author	Mahjabin, Mohammad Ratul
dc.contributor.author	Rahman, Md Sabbir
dc.contributor.author	Raf, Mohtasim Hadi
dc.date.accessioned	2024-01-18T05:35:56Z
dc.date.available	2024-01-18T05:35:56Z
dc.date.issued	2023-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/2051
dc.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	en_US
dc.description.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.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild	en_US
dc.type	Thesis	en_US