Evidence
Sci Rep. 2024 Apr 5;14(1):8020. doi: 10.1038/s41598-024-58249-7.
ABSTRACT
The two-spotted spider mite (TSSM), Tetranychus urticae, is among the most destructive piercing-sucking herbivores, infesting more than 1100 plant species, including numerous greenhouse and open-field crops of significant economic importance. Its prolific fecundity and short life cycle contribute to the development of resistance to pesticides. However, effective resistance loci in plants are still unknown. To advance research on plant-mite interactions and identify genes contributing to plant immunity against TSSM, efficient methods are required to screen large, genetically diverse populations. In this study, we propose an analytical pipeline utilizing high-resolution imaging of infested leaves and an artificial intelligence-based computer program, MITESPOTTER, for the precise analysis of plant susceptibility. Our system accurately identifies and quantifies eggs, feces and damaged areas on leaves without expert intervention. Evaluation of 14 TSSM-infested Arabidopsis thaliana ecotypes originating from diverse global locations revealed significant variations in symptom quantity and distribution across leaf surfaces. This analytical pipeline can be adapted to various pest and host species, facilitating diverse experiments with large specimen numbers, including screening mutagenized plant populations or phenotyping polymorphic plant populations for genetic association studies. We anticipate that such methods will expedite the identification of loci crucial for breeding TSSM-resistant plants.
PMID:38580663 | DOI:10.1038/s41598-024-58249-7
Add to Google Keep
Estimated reading time: 4 minute(s)
Latest: Psychiatryai.com #RAISR4D
Cool Evidence: Engaging Young People and Students in Real-World Evidence ☀️
Real-Time Evidence Search [Psychiatry]
AI Research [Andisearch.com]
Automated imaging coupled with AI-powered analysis accelerates the assessment of plant resistance to Tetranychus urticae
🌐 90 Days
Evidence Blueprint
Automated imaging coupled with AI-powered analysis accelerates the assessment of plant resistance to Tetranychus urticae
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence