Accurate detection of apple blossoms is critical for monitoring flowering status and optimizing agricultural management. Traditional methods often fail to address challenges such as overlapping petals and environmental variability, leading to inefficiency and inaccuracy. In this paper, LEAF-Net, a modified YOLOv11-based target detection model, is proposed. The original C3k2 module in YOLOv11 lacks a targeted attention mechanism and exhibits insufficient enhancement of key features such as petal edges. Therefore, we propose our model, LEAF-Net, which incorporates a Multi-scale Attention Enhanced Block (MAEB) that enhances edge feature extraction through a hierarchical attention mechanism and reconstructs the C3k2 module. A Frequency-aware Feature Pyramid Network (Freq-FPN) that optimizes multi-scale feature fusion while preserving high-frequency details; and a comprehensive apple blossom dataset capturing diverse growth stages and environmental conditions. To address the dataset deficiencies, a specialized apple blossom dataset with complex backgrounds is constructed. Experimental results demonstrate state-of-the-art performance, with LEAF-Net achieving 90.4% mAP50 and 70.4% mAP50-95, significantly outperforming existing benchmarks. The framework’s computational efficiency (7.1 GFLOPs) and adaptability make it suitable for real-time deployment in precision agriculture. These advancements provide an extensible framework for precision orchard surveillance, thereby paving the way for their adaptive deployment in diverse agricultural automation contexts.

Accurate fruit detection in complex orchard environments remains challenging due to variable lighting conditions and weather factors. This paper proposes an optimized contour segmentation model for green spherical fruits (apples and persimmons) based on the E2EC framework. The model employs DLA34 as the backbone network for feature extraction enhanced by a path aggregation balanced feature pyramid network (PAB FPN) with embedded attention mechanisms to refine feature representation. For contour segmentation, we introduce a Cycle MLP Aggregation Deformation (CMAD) module that incorporates cycleMLP to expand the receptive field and improve contour accuracy. Experimental results demonstrate the model’s effectiveness, achieving average precision (AP) and average recall (AR) of 75.5% and 80.4%, respectively, for green persimmons and 57.8% and 64.0% for green apples, outperforming previous segmentation methods. These advancements contribute to the development of more robust smart agriculture systems.