# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 1 Introduction  
Huanglongbing (HLB), also known as citrus greening disease, is a highly infectious bacterial disease caused by the phloem-limited bacterium _Candidatus Liberhatter_ spp., often referred to as the cancer of citrus [1]. The piercing-sucking feeding behavior of the Asian citrus psyllid exacerbates the spread of HLB, making it the primary vector of this disease [2]. Consequently, analyzing the feeding behavior of the Asian citrus psyllid and exploring control measures have become focal points of research [3]. However, the piercing-sucking behavior of these insects is concealed within plant tissues, making direct external observation challenging. The electrical penetration graph (EPG) technique [4, 5], as a precise approach for recording the waveforms generated by insect feeding behavior, has become a primary method for studying the behavior of piercing-sucking insects by accurately interpreting the EPG waveforms [6]. The principle of the EPG technique involves connecting an insect with piercing mouthparts and a plant to electrodes, forming part of an electrical circuit. One electrode is attached to the insectand the other to the plant. These electrodes are connected to an electronic measurement circuit, referred to as the primary circuit. A user-controlled power source (Vs) provides alternating current (AC), direct current (DC), or a combination of AC and DC voltage to the circuit [6]. One lead from the power source is grounded, while the other is connected to the plant, typically the growth medium for the plant, such as soil. The insect on the plant is connected to a fine, flexible gold wire electrode that leads to the signal measurement point (M) of the circuit, which serves as the input to a primary amplifier (with a gain of 50 times for DC systems) [7]. According to [PERSON]'s law, the current passing through each serial component of the circuit is the same, and the voltage differences corresponding to various insect behaviors are displayed in real time as different voltage curves, each representing distinct insect activities [8]. Currently, EPG technology faces challenges in practical applications due to its labor-intensive and time-consuming data processing tasks [9], which require experienced personnel for waveform interpretation and classification. Although experts have attempted to incorporate machine learning methods for waveform recognition, the accuracy of these methods often falls short of practical requirements [10]. Additionally, the complex data processing methods limit the high-throughput analysis and broader application of this technology. Therefore, there is an urgent need to further explore and develop more efficient data processing methods to enhance the application efficiency of EPG technology in studying the behavior of piercing-sucking insects, thereby providing effective support for controlling diseases such as HLB. In this respect, extensive research has been conducted on the classification of insect EPG signals. Due to the large volume of data, [PERSON] employed Hidden Markov Models (HMM) and Fourier transform methods to process raw EPG data. They performed Fourier transform on unannotated EPG signals of psyllid feeding states and used machine learning methods to classify various feeding states with an accuracy of 97.4% [11]. However, their study only identified six waveform types and did not address the most complex A-wave. Based on existing research, the waveforms were generally categorized into six feeding states: salivation and stylet pathway (C), first contact with phloem (D), phloem salivation (E1), phloem ingestion (E2), xylem ingestion (G), or no probing (NP). [PERSON] used fractal-dimension-Hilbert-Huang transform and decision trees to classify and recognize aphid EPG signals, achieving an average recognition accuracy of 91.43% [5], which needs further optimization. In addition, research has shown that the sample size of various waveforms in EPG signals may be a significant factor affecting recognition accuracy [12].