# Trunk Borer Identification Based on Convolutional Neural Networks
## 1 Introduction  
Forestry resources are extremely important for the comprehensive development of China and the stability of the ecological environment. However, the forest is extremely vulnerable to the destruction of the trunk borer. Timely detection and early treatment of pests are undoubtedly the biggest difficulty [1], followed by continuous updates and iterations of monitoring and identification technology for pests, aiming to address this hidden danger. Over the years, trunk borers have caused serious damage to forests, not only causing water loss and nutrient loss of trees, but also endangering the growth of the main trunk of trees [2]. Trunk borers generally live in hosts in the early days, nibbling on branches to obtain nutrients and destroying the tissue structure of trees. Because it is difficult to find and hard to control, the challenge is posed to the detection of pests. Traditional pest detection methods make it difficult to find pests which are hidden in the trunk, and thus, foresters miss the best control period. Therefore, the harm is further aggravated, resulting in irreparable losses, especially in rare trees. In recent years, with the application of acoustic technology in pest control, new directions in thinking [3, 4] for the early identification of pests have been provided. Sound detection has gradually become a new type of pest detection method [5, 6]. Compared with traditional methods such as spot detection and pheromone trapping, the method of detecting the activity sound of pests in trees has the advantages of easy convenience, early warning time, high efficiency and low destructiveness [7, 8]. As early as the 1920s, people began to pay attention to the acoustic characteristics of insects, classifying them as characteristic information of insects and testing them [9]. With the development of technology, the use of electronic devices for food acoustic detection of fruit pests has provedfor the first time the feasibility of detecting pests using acoustic characteristics. Through long-term technical progress and experiments, the recognition model has developed from a simple hidden Markov model and Gaussian mixture model to full connection depth neural network [10; 11]. The accuracy of identification has been continuously improved, meeting the practical application requirements [12]. The popularity of deep neural networks has brought inspiration for researchers. The rise of computer technology and the development of electronic technology, as well as the innovation and progress of sound detection equipment and analysis methods, have promoted the development of acoustic testing [13; 14]. At home and abroad pest monitoring and identification based on voice recognition technology is also in full swing [15]. Most of the research objects are the hidden activities of pests such as storage pests, wood quarantine pests, fruit pests and trunk borers. In the early days, the microphone was directly fixed on the surface of the trunk to record the sound transmitted into the air, and the effective feature information obtained was very one-sided and not enough to judge the presence of pests. The use of piezoelectric sensors to collect vibrational sound generated by pests' activity inside the trunk improves monitoring sensitivity and reduces the interference of environmental noise. The vibration signals of the trunk borer are recorded by the sensor and saved in an audio format [16; 17], where feature points are identified and classified using deep learning techniques. In the sound recognition of pests, the random short pulses in the audio are the key point of identification [18; 19] because each pulse represents the activity of pests in the tree. Through the combination of three processes of feature extraction, the audio can be learned and the prediction probability can be given a corresponding confidence score to determine whether pests exist [20; 21; 22; 23; 24]. This study focus on _Agrilus planipennis_ Fairmaire [25].