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Hong SJ, Nam I, Kim SY, Kim E, Lee CH, Ahn S, Park IK, Kim G. Automatic Pest Counting from Pheromone Trap Images Using Deep Learning Object Detectors for Matsucoccus thunbergianae Monitoring. Insects 2021; 12:insects12040342. [PMID: 33921492 PMCID: PMC8068825 DOI: 10.3390/insects12040342] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 11/16/2022]
Abstract
Simple Summary The black pine bast scale, Matsucoccus thunbergianae, is a forest pest that causes widespread damage to black pine; therefore, monitoring this pest is necessary to minimize environmental and economic losses in forests. However, monitoring insects in pheromone traps performed by humans is labor intensive and time consuming. To develop an automated monitoring system, we aimed to develop algorithms that detect and count M. thunbergianae from images of pheromone traps using deep-learning-based object detection algorithms. Object detection models based on deep learning neural networks under various conditions were trained, and the performances of detection and counting were compared and evaluated. In addition, the models were trained to detect small objects well by cropping images into multiple windows. As a result, the algorithms based on deep learning neural networks successfully detected and counted M. thunbergianae. These results showed that accurate and constant pest monitoring is possible using the artificial-intelligence-based methods we proposed. Abstract The black pine bast scale, M. thunbergianae, is a major insect pest of black pine and causes serious environmental and economic losses in forests. Therefore, it is essential to monitor the occurrence and population of M. thunbergianae, and a monitoring method using a pheromone trap is commonly employed. Because the counting of insects performed by humans in these pheromone traps is labor intensive and time consuming, this study proposes automated deep learning counting algorithms using pheromone trap images. The pheromone traps collected in the field were photographed in the laboratory, and the images were used for training, validation, and testing of the detection models. In addition, the image cropping method was applied for the successful detection of small objects in the image, considering the small size of M. thunbergianae in trap images. The detection and counting performance were evaluated and compared for a total of 16 models under eight model conditions and two cropping conditions, and a counting accuracy of 95% or more was shown in most models. This result shows that the artificial intelligence-based pest counting method proposed in this study is suitable for constant and accurate monitoring of insect pests.
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Affiliation(s)
- Suk-Ju Hong
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (S.-J.H.); (S.-Y.K.); (E.K.); (C.-H.L.); (S.A.)
| | - Il Nam
- Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea; (I.N.); (I.-K.P.)
| | - Sang-Yeon Kim
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (S.-J.H.); (S.-Y.K.); (E.K.); (C.-H.L.); (S.A.)
| | - Eungchan Kim
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (S.-J.H.); (S.-Y.K.); (E.K.); (C.-H.L.); (S.A.)
- Global Smart Farm Convergence Major, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Chang-Hyup Lee
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (S.-J.H.); (S.-Y.K.); (E.K.); (C.-H.L.); (S.A.)
- Global Smart Farm Convergence Major, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Sebeom Ahn
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (S.-J.H.); (S.-Y.K.); (E.K.); (C.-H.L.); (S.A.)
| | - Il-Kwon Park
- Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea; (I.N.); (I.-K.P.)
- Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
| | - Ghiseok Kim
- Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea; (S.-J.H.); (S.-Y.K.); (E.K.); (C.-H.L.); (S.A.)
- Global Smart Farm Convergence Major, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Research Institute of Agriculture and Life Science, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
- Correspondence: ; Tel.: +82-2-880-4603
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