AR and AI automatically diagnose agromyzid leafminer damage levels

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, a team of researchers from China developed an advanced survey system — combining augmented reality (AR) glasses with a camera and an artificial intelligence (AI) algorithm — 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,” shares corresponding author Qing Yao, a professor at Zhejiang Sci-Tech University in China. “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 research team proposed the DeepLab-Leafminer model 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,” explains Yao.

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. Moreover, further test revealed a 92.38% diagnosis accuracy of leafminer damage levels. A mobile application and a web platform were developed to assist surveyors in displaying the diagnostic results of leafminer damage levels. 

The team published their findings in the Journal of Integrative Agriculture.

“This method of can also be utilized to automatically diagnose pest and disease damage levels in other crops based on leaf images,” adds co-corresponding author Wanxue Liu, a professor at the Chinese Academy of Agricultural Sciences.

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Contact the author: Zhongru Ye, Mobile: +86-13735927281, E-mail: zhongruYE@163.com;

Correspondence Qing Yao, E-mail: q-yao@zstu.edu.cn; Wanxue Liu, E-mail: liuwanxue@caas.cn

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