Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small Objects-Reference-Cited by-同舟云学术

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Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small Objects

Published:2023 Issue:5 Volume:14 Page:907-917
ISSN:1798-2340
Container-title:Journal of Advances in Information Technology
language:
Short-container-title:JAIT

Author:

Quach Luyl-Da,Quoc Khang Nguyen,Quynh Anh Nguyen,Ngoc Hoang Tran

Publisher

Engineering and Technology Publishing

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Information Systems,Software

Link

http://www.jait.us/uploadfile/2023/JAIT-V14N5-907.pdf

Reference44 articles.

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2. [2] P. Varma, "Enhancing rice productivity and food security: A study of the adoption of the System of Rice Intensification (SRI) in selected states of India," Centre for Management in Agriculture Indian Institute of Management Ahmedabad, vol. 74, no. 1, pp. 37-39, 2016.

3. [3] C. R. Rahman et al., "Identification and recognition of rice diseases and pests using convolutional neural networks," Biosystems Engineering, vol. 194, pp. 112-120, Jun. 2020.

4. [4] S. Ghosal and K. Sarkar, "Riceleaf diseases classification using CNN with transfer learning," in Proc. 2020 IEEE Calcutta Conference (CALCON), IEEE, 2020, pp. 230-236.

5. [5] G. Latif, S. E. Abdelhamid, R. E. Mallouhy, J. Alghazo, and Z. A. Kazimi, "Deep learning utilization in agriculture: Detection of rice plant diseases using an improved CNN model," Plants, vol. 11, no. 17, 2230, Aug. 2022.

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