Segmentation and flowering period quantification of mung bean flowers in unmanned aerial vehicle images using IDCA-Unet

Mung bean (Vigna radiata L.) is one of the most favorite crop plants worldwide. It is often required for the precise quantification of the flowering traits during crop phenotyping and breeding. Particularly, the flowering synchrony can directly influence the population trait uniformity and varietal maturity consistency. Thereby the breeding materials can be refined to evaluate the varietal adaptability. However, the conventional semantic segmentation of flowers cannot fully meet the large-scale quantitative analysis in recent years. Some challenges remain in the low contrast with the background, such as the complex inflorescence structure of the mung bean plants, the minute size of the floral organs, their similar coloration to leaves and stems, coupled with the intense lighting variations, extensive occlusions, and overlapping in field environments. Alternatively, deep learning can be expected for the image segmentation of small targets in complex agricultural scenarios. Therefore, it is very necessary for sufficient feature extraction and model generalization to avoid the spatial information loss. In this study, an IDCA-UNet model was proposed to integrate a dual channel attention (DCA) mechanism and instance normalization (IN). The high-resolution images of the mung bean plants were also captured at the flowering and pod-setting stage using an unmanned aerial vehicle (UAV) remote sensing platform. According to the classic U-Net architecture, the DCA module was employed to dynamically aggregate some features from the global average pooling and max pooling, in order to realize the adaptive feature enhancement in the channel dimension. The high-resolution features effectively improved the sensitivity and recognition for the tiny flowers. Simultaneously, the Instance Normalization layers were introduced to replace the batch normalization layers. The unstable statistical estimation was avoided to reduce the internal covariate shift during small-batch training. The robustness and generalization were enhanced under complex and variable field conditions. Experimental results demonstrate that the IDCA-UNet significantly outperformed the various models, including the ResNet-UNet, YOLOv11, and DeepLabV3+ in the mung bean flower segmentation task, with the mIoU, mAP, and F1 scores of 88.95%, 93.47%, and 93.79%, respectively. There were the improvements of 1.62, 1.48, and 0.72 percentage points, compared with the VGG16-UNet benchmark model with the Focal loss. The high-precision segmentation was further quantitatively analyzed to study the dynamic growth patterns of the mung bean plants at the flowering and pod-setting stage. The flowering dynamics exhibited the "Λ"-shaped growth curve with a concentrated peak flowering period after daily flower count statistics and fitting, indicating the strong flowering synchrony within the population. Therefore, important evidence can be provided to assess the varietal maturity, consistency, and suitability for mechanical harvesting. The mung bean flower segmentation can also offer a practical cross-platform application for the organ-level crop phenotyping. Multi-source remote sensing data and temporal analysis can be integrated for environmental forecasting in the future. Thereby, the finding can serve as the robust theoretical and technical support to accurately assess the crop growth status, particularly for the high yield and efficiency breeding in precision agriculture.