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Sci Rep

Title:		Deep learning-based system development for black pine bast scale detection
Author(s):		Yun W; Kumar JP; Lee S; Kim DS; Cho BK;
Address:		"Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-ro, Yuseonggu, Daejeon, 34134, Korea. School of Computer Science and Engineering, VIT-AP University, Near Vijayawada, Vijayawada, Andhra Pradesh, India. Forest Biomaterials Research Center, National Institute of Forest Science, 672 Jinju-daero, Jinju-si, 52817, Korea. Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-ro, Yuseonggu, Daejeon, 34134, Korea. chobk@cnu.ac.kr. Department of Smart Agriculture Systems, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Korea. chobk@cnu.ac.kr"
Journal Title:		Sci Rep
Year:		2022
Volume:		20220112
Issue:		1
Page Number:		606 -
DOI:		10.1038/s41598-021-04432-z
ISSN/ISBN:		2045-2322 (Electronic) 2045-2322 (Linking)
Abstract:		"The prevention of the loss of agricultural resources caused by pests is an important issue. Advances are being made in technologies, but current farm management methods and equipment have not yet met the level required for precise pest control, and most rely on manual management by professional workers. Hence, a pest detection system based on deep learning was developed for the automatic pest density measurement. In the proposed system, an image capture device for pheromone traps was developed to solve nonuniform shooting distance and the reflection of the outer vinyl of the trap while capturing the images. Since the black pine bast scale pest is small, pheromone traps are captured as several subimages and they are used for training the deep learning model. Finally, they are integrated by an image stitching algorithm to form an entire trap image. These processes are managed with the developed smartphone application. The deep learning model detects the pests in the image. The experimental results indicate that the model achieves an F1 score of 0.90 and mAP of 94.7% and suggest that a deep learning model based on object detection can be used for quick and automatic detection of pests attracted to pheromone traps"
Keywords:
Notes:		"PubMed-not-MEDLINEYun, Wonsub Kumar, J Praveen Lee, Sangjoon Kim, Dong-Soo Cho, Byoung-Kwan eng Research Support, Non-U.S. Gov't England 2022/01/14 Sci Rep. 2022 Jan 12; 12(1):606. doi: 10.1038/s41598-021-04432-z"