PVDT: A smart tool for rapid detection of pollen viability

Pollen viability is a critical determinant of maize reproductive capacity and genetic potential, directly influencing pollination efficiency, seed set, and overall crop yield. While traditional detection methods are labor-intensive and time-consuming, rapid alternatives like flow cytometry remain prohibitively expensive, and existing machine vision approaches lack specialized tools. To address these challenges, the study introduces “AIpollen” software and a cost-effective Pollen Viability Detection Tool (PVDT) device. The software employs a dual-pathway detection system: (1) the YOLOv10-CBAM model, which achieves 90%–98% accuracy even under challenging conditions (e.g., variable lighting, high pollen density and low image quality), outperforming existing advanced models; and (2) the Pollen Microscopic Image Segmentation Algorithm (PMISA). When combined with a Residual Dual-Attention Gated Network (RDAGNet) model, PMISA mitigates performance degradation in high-density or low-quality images, boosting detection accuracy by approximately 7%. Field trials utilizing the PVDT evaluated pollen viability in response to heat stress for 350 maize inbred lines, supporting the exploration of loci associated with pollen viability through GWAS. Furthermore, Transfer learning enabled the detection of pollen viability across ten additional plant species (e.g., wheat, peas, alfalfa, potato, tobacco) with favorable detection performance.