Abstract: China has a vast territory and abundant forest resources. With the invasion of alien insects, the outbreak rate of tree pests and diseases in China has increased year by year, causing a huge impact on China’s ecology and economy. However, monitoring of tree pests and diseases can only be carried out accurately after the outbreak of pests and diseases in some areas. If tree pests and diseases have not been discovered and treated in the early stages, they may cause an explosive and serious situation, and the prevention and control work will be in a passive situation. Therefore, it is particularly important to accurately detect internal defects of wood caused by diseases and pests. For earlier and more accurate predictions of tree pests and diseases, regular testing and monitoring are required. The early traditional methods such as visual inspection, percussion discrimination and anatomical observation have the disadvantages of low accuracy, poor timeliness and irreversible damage to trees. In recent years, modern detection methods have been developed, including the ultrasonic testing methods with good directivity and high energy, the lowcost, highly penetrating stress wave testing methods and the electromagnetic wave testing methods with accurate imaging suitable for different environments. Different detection methods have corresponding imaging algorithms, such as travel time method, frequency domain analysis method and Born approximation method. These imaging algorithms have advantages in resolution, accuracy, precision, and computational speed. By summarizing the development status of these three detection methods, understanding the advantages and disadvantages of various detection methods will help to better detect the pests and diseases of trees under different conditions, turning passive control after the outbreak of tree pests and diseases into active prevention before the outbreak of tree pests and diseases so as to make early response plans.

Key words: Diseases and pests, wood, internal defects, nondestructive testing, accurate