Construction of an Accurate Evaluation Model for Apple Flowering Period Based on Multimodal Data

Flowering period management is a critical component of orchard production, significantly influencing the accuracy and timeliness of agricultural decisions such as flower and fruit thinning, yield stabilization, improvement in fruit commodity value, and control of mold core disease. Aiming at the problems of traditional flowering period judgment relying on manual experience, strong subjectivity, low efficiency, and difficulty in large-scale implementation, this study proposes an accurate evaluation model for apple flowering period based on near–far view multimodal visual data. A dedicated near–far view combined vision acquisition system was built to synchronously obtain panoramic images of fruit tree canopies and high-definition close-up images of single flowers/clusters, constructing a multimodal dataset covering the canopy spatial structure and fine floral organ morphology. YOLOv5s and ResNet-50 were employed to extract macro flowering proportion features from far views and micro morphological features from near views, respectively. A feature fusion strategy was introduced to realize the deep fusion of macro–micro features, and finally, a multimodal flowering period classification model was constructed to accurately divide the apple flowering period into four stages: bud stage, initial bloom stage, full bloom stage and late bloom stage. The overall recognition accuracy of the model reached 95.7%. The accurate apple flowering period evaluation system built based on this model has realized the paradigm shift in flowering period judgment from “qualitative manual experience” to “accurate quantification by machine vision”, providing a scientific time window basis for core orchard operations such as pre-flower re-pruning, flowering pollination, fruit setting evaluation and fruit thinning and bagging, and effectively promoting the intelligent and operational development of orchard management.