Screening Verticillium wilt-resistant germplasm by monitoring the time-series chlorophyll content of cotton canopies via a UAV-based high-throughput platform

Verticillium wilt (VW) is a highly detrimental disease of cotton that causes significant reductions in yield and fiber quality. Efficient and accurate screening of VW-resistant varieties is essential for cotton breeding and production. However, traditional identification methods, such as manual observation, are inefficient and costly. Unmanned aerial vehicle (UAV) and remote sensing technologies have opened new insights into the screening of field crops for disease-resistant germplasm. This study utilized a UAV multispectral platform to collect data from five growth stages of 150 cotton varieties with different VW resistances. The normalized difference vegetation index (NDVI) was identified as a reliable predictor of chlorophyll levels through hierarchical segmentation analysis. We further compared four deep learning models for chlorophyll monitoring: 1D-CNN, CNN-BiLSTM, CNN-BiLSTM-Adaboost, and CNN-BiLSTM-Attention, with the CNN-BiLSTM-Attention model performing best (R² = 0.92). The optimum model was then used to invert the extent of VW infection using single- and multi-period chlorophyll, and the latter was found to have the best results with the highest R² value of 0.96. Multidimensional clustering of chlorophyll content over multiple periods was used to screen different cotton VW-resistant germplasm, and the ISODATA cluster method outperformed the other three methods (K-means, K means++, and GMM). This study highlights that combining a UAV multispectral platform with an accurate chlorophyll inversion model can enable high-throughput assessment of the cotton VW infection in the field, providing a powerful tool for screening cotton VW-resistant germplasm and thus supporting cotton breeding efforts.