@article{Li2026, 
author = {Jun Li and Zhe Ma and Jiaquan Lin and Qian Wang and Xiaoting Lin and Juan Xia and Bolai Xin},
title = {FQGR-net: Morphology-based litchi flower quantification and gender recognition},
year = {2026},
journal = {Plant Phenomics},
volume = {8},
number = {3},
pages = {100217},
keywords = {Litchi, Flower count, Flower gender, Deep learning, Phenotyping},
url = {https://www.sciopen.com/article/10.1016/j.plaphe.2026.100217},
doi = {10.1016/j.plaphe.2026.100217},
abstract = {As a crucial agricultural crop in China, litchi exhibits a biennial bearing pattern with alternating high-yield and low-yield cycles, known as on-year and off-year respectively. Research has identified unstable floral initiation as the primary cause of irregular fruiting in mid-to-late maturing cultivars. Rapid and accurate quantification of female to male flower ratios during the flowering phase enables targeted management strategies to optimize floral development and enhance fruit-setting rates. This study proposes Flower Quantification and Gender Recognition Network (FQGR-Net), a three-branch neural network architecture for simultaneous classification and counting of female and male flowers. Through module-level optimization, FQGR-Net improves both counting accuracy and computational efficiency, achieving average MAE of and RMSE of across categories in experiments conducted on the self-constructed dataset. Comparative experiments with other deep neural network models on public datasets show the proposed method achieves optimal performance. A regression analysis between predictions and ground truth produces values of and for female and male flower quantification respectively. A dedicated litchi flower phenotyping analyzer was developed to address the technological gap in automated floral census systems. Field trials demonstrated over accuracy in female/male flower counting.}
}