| Title: | Automatic Pest Counting from Pheromone Trap Images Using Deep Learning Object Detectors for Matsucoccus thunbergianae Monitoring |
| Author(s): | Hong SJ; Nam I; Kim SY; Kim E; Lee CH; Ahn S; Park IK; Kim G; |
| Address: | "Department of Biosystems Engineering, College of Agriculture and Life Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea. Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea. 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" |
| ISSN/ISBN: | 2075-4450 (Print) 2075-4450 (Electronic) 2075-4450 (Linking) |
| 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" |
| Keywords: | Cnn Faster R-CNN Matsucoccus thunbergianae Ssd deep learning insect counting object detection pest monitoring sex pheromone trap; |
| Notes: | "PubMed-not-MEDLINEHong, Suk-Ju Nam, Il Kim, Sang-Yeon Kim, Eungchan Lee, Chang-Hyup Ahn, Sebeom Park, Il-Kwon Kim, Ghiseok eng 2020185B10-2022-AA02/Korea Forest Service/ Switzerland 2021/05/01 Insects. 2021 Apr 12; 12(4):342. doi: 10.3390/insects12040342" |
