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With global climate change and the deterioration of the ecological environment, the safety of hydraulic engineering faces severe challenges, among which soil-dwelling termite damage has become an issue that cannot be ignored. Reservoirs and embankments in China, primarily composed of earth and rocks, are often affected by soil-dwelling termites, such as Odontotermes formosanus and Macrotermes barneyi. Identifying soil-dwelling termite damage is crucial for implementing monitoring, early warning, and control strategies. This study developed an improved YOLOv8 model, named MCD-YOLOv8, for identifying traces of soil-dwelling termite activity, based on the Monte Carlo random sampling algorithm and a lightweight module. The Monte Carlo attention (MCA) module was introduced in the backbone part to generate attention maps through random sampling pooling operations, addressing cross-scale issues and improving the recognition accuracy of small targets. A lightweight module, known as dimension-aware selective integration (DASI), was added in the neck part to reduce computation time and memory consumption, enhancing detection accuracy and speed. The model was verified using a dataset of 2096 images from the termite damage survey in hydraulic engineering within Hubei Province in 2024, along with images captured by drone. The results showed that the improved YOLOv8 model outperformed four traditional or enhanced models in terms of precision and mean average precision for detecting soil-dwelling termite damage, while also exhibiting fewer parameters, reduced redundancy in detection boxes, and improved accuracy in detecting small targets. Specifically, the MCD-YOLOv8 model achieved increases in precision and mean average precision of 6.4% and 2.4%, respectively, compared to the YOLOv8 model, while simultaneously reducing the number of parameters by 105,320. The developed model is suitable for the intelligent identification of termite damage in complex environments, thereby enhancing the intelligent monitoring of termite activity and providing strong technical support for the development of termite control technologies. Full article

Subterranean termites of the species Reticulitermes speratus kyushuensis Morimoto (Isoptera: Rhinotermitidae) are notoriously destructive soil-dwelling pests that feed on the cellulosic wood biomass. This leads to tremendous losses of forest trees such as Pinus densiflora Siebold and Zucc. (Pinales: Pinaceae) and precious wooden structures of cultural heritage. This study investigated the efficacy of chitinase and protease produced by Bacillus licheniformis PR2 as cuticle-degrading enzymes for the biocontrol of worker termites. Bacillus licheniformis PR2 produced a strong chitinase and protease activity up to a maximum of 82.3 unit/mL and 35.9 unit/mL, respectively, and caused a lethal effect on termites under laboratory conditions. Treatment of termites with the bacterial broth culture and the crude enzyme fraction of B. licheniformis PR2 resulted in a maximum mortality rate (with a median lethal time (ET50)) of 83.3% (3 h, 36 min) and 88.9% (2 h, 59 min), respectively. The termites treated with B. licheniformis PR2 exhibited loss of setae, disintegration of epicuticle, rupturing of procuticle, and swelling at sockets. The degradation of cuticular chitin and glycoprotein polymers in the termite cuticle by chitinase and protease enzymes produced by B. licheniformis PR2 represents an effective eco-friendly strategy for controlling termite damage in Korean cultural heritage sites and forests. Full article