A Method for Classifying Wood-Boring Insects for Pest Control Based on Deep Learning Using Boring Vibration Signals with Environment Noise.

Simple Summary: Wood-boring pests pose a significant threat to the health of trees in forest and urban ecosystems. Their larvae live deep within the xylem of tree trunks, where visible signs of their activity on the trunk surface are minimal. Traditional monitoring methods are time-consuming and labor-intensive, making it difficult to detect infestations early, thereby complicating pest control efforts. Taking the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae), commonly found on ash trees (Fraxinus chinensis Roxb) in northern China, as examples, acoustic sensors can be used to collect the feeding vibration signals of their larvae, providing a new approach to pest identification. This paper proposes a deep learning recognition model, BorerNet, which uses these boring vibration signals as input and incorporates an attention mechanism. Experimental results show that this model achieves high recognition accuracy. The method can provide technical support for the identification and control of wood-boring pests. Wood-boring pests are difficult to monitor due to their concealed lifestyle. To effectively control these wood-boring pests, it is first necessary to efficiently and accurately detect their presence and identify their species, which requires addressing the limitations of traditional monitoring methods. This paper proposes a deep learning-based model called BorerNet, which incorporates an attention mechanism to accurately identify wood-boring pests using the limited vibration signals generated by feeding larvae. Acoustic sensors can be used to collect boring vibration signals from the larvae of the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae). After preprocessing steps such as clipping and segmentation, Mel-frequency cepstral coefficients (MFCCs) are extracted as inputs for the BorerNet model, with noisy signals from real environments used as the test set. BorerNet learns from the input features and outputs identification results. The research findings demonstrate that BorerNet achieves an identification accuracy of 96.67% and exhibits strong robustness and generalization capabilities. Compared to traditional methods, this approach offers significant advantages in terms of automation, recognition efficiency, and cost-effectiveness. It enables the early detection and treatment of pest infestations and allows for the development of targeted control strategies for different pests. This introduces innovative technology into the field of tree health monitoring, enhancing the ability to detect wood-boring pests early and making a substantial contribution to forestry-related research and practical applications. [ABSTRACT FROM AUTHOR]