Litchi is a major tropical and subtropical crop in China, covering approximately 0.523 million hectares. The industry faces a pronounced alternate bearing pattern, with yields fluctuating sharply between "on-years" and "off-years" — 2024 production was estimated at 1.781 million tons, a 45.94% decrease from the previous year. Unstable floral initiation is the primary cause, especially in mid- to late-maturing cultivars. Because a high ratio of female to male flowers generally correlates with improved fruit set, rapid and accurate quantification of flower sex ratios during flowering is essential for targeted panicle management. Traditional manual counting is labor-intensive and prone to subjective errors, while existing automated methods mainly handle single-class detection or require detached floral samples, failing to support non-destructive monitoring in natural growth states. Because of these challenges, there is a need for in-depth research on multi-class, in-situ litchi flower classification and counting.
Researchers from the College of Engineering at South China Agricultural University (Guangzhou, China), the Guangdong Laboratory for Lingnan Modern Agriculture, and the National Key Laboratory of Agricultural Equipment Technology published (DOI: 10.1016/j.plaphe.2026.100217) the study in Plant Phenomics on 21 September 2026. The work presents FQGR-Net, a three-branch neural network for simultaneous classification and counting of female and male litchi flowers, along with a portable phenotype analyzer for orchard deployment.
FQGR-Net builds on a VGG16 backbone with three parallel branches: a foreground segmentation branch guided by a Frequency-Aware Dilated Module (FADM), a refined density regression branch using LiteIncepDWConv (LIDWC) and DySampleFusion, and a coarse density estimation branch with a Multi-Scale Fusion Module (MSFM). The FADM captures intra-class multi-scale features and suppresses background noise, while the MSFM aggregates hierarchical features from different network depths to address scale variations. The team also extended traditional point-level annotation to multi-class labeling, marking male flowers at stamen filament convergence centers and female flowers at stigma junctions, which improves occlusion resistance. The lightweight convolution and dynamic upsampling achieve a better trade-off between accuracy and model size, reducing parameters by 28.746% compared with standard convolutions and making edge deployment feasible. Ablation studies showed that combining MSFM and FADM improved mean absolute error (MAE)_A and root mean squared error (RMSE)_A by 35.616% and 35.663% over the baseline. The handheld analyzer integrates an NVIDIA Jetson Orin NX Super computing platform, an 8-megapixel IMX415 CMOS (complementary metal-oxide-semiconductor) sensor, and a 5.0-inch touchscreen, running models via ONNX Runtime (Open Neural Network Exchange Runtime) with CUDA (Compute Unified Device Architecture) acceleration. Field trials on 50 samples across multiple cultivars achieved 85.81% and 87.50% accuracy for female and male flower identification, respectively.
The authors said the work addresses a critical gap in automated floral census systems. They explained that accurate, non-destructive estimation of female-to-male flower ratios gives growers and breeders a practical tool for precision flower thinning, variable-rate management, and selection of parents with high fruit-set potential. They added that extending point-level annotation to multi-class counting improves deployment flexibility and could be adapted to other flowering crops with similar dense, small-target morphology, supporting broader applications in precision agriculture and yield estimation.
The technology could guide variable-rate flower-thinning machinery, adjusting thinning intensity to local flower density and reducing mechanical damage to remaining flowers. For horticultural research on plant growth regulators and floral sex differentiation, FQGR-Net enables rapid, reliable quantification of treated panicles, saving time and labor while reducing counting errors. The authors noted that combining panicle phenotypes with genome sequencing could help map quantitative trait loci (QTLs) related to flower number and female flower ratio, supporting breeding of offspring with high fruit-set rates and stress resistance. Future work will expand training data from overexposed and low-light conditions, incorporate 3D flower panicle reconstruction, and investigate multi-class counting in intercropped systems.
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References
DOI
10.1016/j.plaphe.2026.100217
Original Source URL
https://doi.org/10.1016/j.plaphe.2026.100217
Funding information
This work was supported in part by National Natural Science Foundation of China under Grant 32501767, the 2025 Basic and Applied Basic Research Special Issue (Doctoral "Sailing" Project) under Grants 2025A04J4678, the Guangdong Laboratory for Lingnan Modern Agriculture under Grants NT2021009, the China Agriculture Research System under Grant CARS-32, the open competition program of top ten critical priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (2022SDZG03), and the Discipline Construction Proiect of South China Agricultural University in 2023 under Grant 2023B10564002.
About Plant Phenomics
Plant Phenomics publishes breakthrough research that integrates genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics and computer science to advance plant phenotyping—from the cellular level to entire populations. The journal tackles key scientific challenges in phenomics by showcasing cutting‑edge technologies for data acquisition, management, interpretation and modelling. Its scope spans practical applications across all plant sciences, aiming to translate novel phenotyping tools into real‑world solutions for breeding, crop management and fundamental plant biology. By fostering interdisciplinary innovation, Plant Phenomics serves as a hub for researchers developing high‑throughput, accurate and scalable methods to decipher complex plant traits, ultimately accelerating progress in agriculture and plant science.