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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.

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.

Matsuone is a well-known sex pheromone of the genus Matsucoccus (Hemiptera: Margarodidae), including species Matsucoccus matsumurae (Kuwana), Matsucoccus resinosae Bean & Goldwin, and Matsucoccus thunbergianae Miller & Park. In this study, we investigated the effects of matsuone dose and trap height on the capture of M. thunbergianae and developed an alternative synthesis of racemic matsuone. In field trapping experiments, M. thunbergianae males showed dose-dependent attraction to (6R,10R/S)-matsuone from 100 μg up to an approximate saturation level of 1,600 μg per rubber septum lure. Traps baited with (6R,10R/S)-matsuone and installed 50 cm above ground level attracted more males than traps 100 and 150 cm above ground level. To reduce synthesis procedures, time, and labor, we developed a new synthetic route to racemic matsuone and conducted field experiments with the product. Although traps baited with the racemic matsuone were less attractive than traps baited with (6R,10R/S)-matsuone synthesized by a previously reported method, the new synthetic route could be an economically favorable alternative to the previous method used in production of lures for field application.

Matsucoccus matsumurae (Kuwana, 1905), commonly known as Japanese pine bast scale, is a destructive pest on pine trees in North America, East Asia, and Northern Europe. The spread of damage to black pine trees, Pinus thunbergii Parl., due to M. matsumurae has been reported throughout Southern and some Eastern and Western coastal regions in Korea, under the name M. thunbergianae, which was described by Miller and Park (1987). Recently, M. thunbergianae was synonymized with M. matsumurae by Booth and Gullan (2006), based on molecular sequences and morphological data. However, M. thunbergianae is still considered a valid species in Korea. Since supporting data for the synonyms are unavailable in any DNA database (e.g., GenBank and BOLD), we performed morphological and molecular comparisons to review the results of Booth and Gullan (2006) using samples of M. matsumurae collected from Japan and topotype materials of M. thunbergianae from Korea. Our study supports the opinion of Booth and Gullan (2006), as the morphological features of the adult female and male of M. thunbergianae are identical to those of M. matsumurae, and DNA sequences (18S and 28S) of M. thunbergianae show identical or very low genetic distances with those of M. matsumurae. Additionally, regional sampling of Korea produced the first documented occurrence of M. matsumurae in Jeju.