From June to July 2024, during greenhouse in Linzhi City (Tsatsum Town) and filed surveillance in Changdu City (Karuo Town and Rumei Town), Xizang, China, we successively found serious infestations of lepidopteran larvae on tomato and potato plants, with mined leaves and attacked fruits. The lepidopteran was identified as Tuta absoluta (Meyrick) using morphology and molecular methods, a major agricultural invasive pest in China. It is the first report that T. absoluta occurred in Xizang, China. In this paper, the morphological characteristics of T. absoluta and its infestation characteristics in Xizang are described. The possibility that the South American tomato leafminer could be introduced into Xizang via India through international trade in agricultural products, or from some domestic regions where the pest has already occurred (Sichuan, Yunnan, etc.) through seedling transportation was discussed and the spread trend was studied and judged. Currently, it is evident that the diffusion trend of T. absoluta is from low-altitude to high-altitude in Xizang. Therefore, in order to prevent the continued introduction and diffusion hazard of the pest, it is necessary to strengthen the inspection and quarantine on agricultural product trade and seedling transportation to block dispersion pathways of T. absoluta, and timely and comprehensive survey of the pest in the entire region, including clarifying its main occurrence regularity, degree of damage, and distribution range and so on, which provides reference for monitoring and controlling agricultural invasive species in Xizang.

Agromyzid leafminers cause significant economic losses in both vegetable and horticultural crops, and precise assessments of pesticide needs must be based on the extent of leaf damage. Traditionally, surveyors estimate the damage by visually comparing the proportion of damaged to intact leaf area, a method that lacks objectivity, precision, and reliable data traceability. To address these issues, an advanced survey system that combines augmented reality (AR) glasses with a camera and an artificial intelligence (AI) algorithm was developed in this study to objectively and accurately assess leafminer damage in the field. By wearing AR glasses equipped with a voice-controlled camera, surveyors can easily flatten damaged leaves by hand and capture images for analysis. This method can provide a precise and reliable diagnosis of leafminer damage levels, which in turn supports the implementation of scientifically grounded and targeted pest management strategies. To calculate the leafminer damage level, the DeepLab-Leafminer model was proposed to precisely segment the leafminer-damaged regions and the intact leaf region. The integration of an edge-aware module and a Canny loss function into the DeepLabv3+model enhanced the DeepLab-Leafminer model's capability to accurately segment the edges of leafminer-damaged regions, which often exhibit irregular shapes. Compared with state-of-the-art segmentation models, the DeepLab-Leafminer model achieved superior segmentation performance with an Intersection over Union (IoU) of 81.23% and an F₁ score of 87.92% on leafminer-damaged leaves. The test results revealed a 92.38% diagnosis accuracy of leafminer damage levels based on the DeepLab-Leafminer model. A mobile application and a web platform were developed to assist surveyors in displaying the diagnostic results of leafminer damage levels. This system provides surveyors with an advanced, user-friendly, and accurate tool for assessing agromyzid leafminer damage in agricultural fields using wearable AR glasses and an AI model. This method can also be utilized to automatically diagnose pest and disease damage levels in other crops based on leaf images.