Automated imaging coupled with AI-powered analysis accelerates the assessment of plant resistance to Tetranychus urticae

Abstract

Abstract The two-spotted spider mite (TSSM), Tetranychus urticae, is one of the most destructive piercing-sucking herbivores, infesting more than 1,100 plant species. The TSSM has evolved a broad tolerance to different plant xenobiotics, influencing its flexibility to adapt to multiple host plants and pesticides. At the same time, the effective resistance loci in plants are still unknown. To find out more about plant-mite correlation, novel approaches are required allowing the efficient screening of large, genetically diverse populations of two interacting species. Here we propose an analytical pipeline based on high-resolution imaging of infested leaves and an artificial intelligence-based computer program, MITESPOTTER, for analysis of plant susceptibility. Our system precisely identifies and quantifies eggs, feces and damaged areas on phenotypically differentiated leaves. The new method was tested on 14 TSSM-infested Arabidopsis thaliana ecotypes derived from diverse world locations and showing remarkable differences in the listed symptoms. The proposed method also demonstrated the ecotype variation in the mite preference to the age of leaf and egg distribution on the ab/adaxial leaf surface. The presented analytical pipeline can be adapted to different pest and host species facilitating diverse experiments with a high number of specimens such as the screening of a large segregating population of plants leading to the identification of loci for efficient breeding of TSSM-resistant plants.