Sustainable precision agriculture is crucial for optimizing resource utilization, reducing chemical inputs, and ensuring global food security. High-precision automatic recognition and monitoring of key crop organs (e.g., wheat heads and flower clusters) serve as the technological foundation for sustainable agricultural management decisions. However, visual perception in natural field environments is highly susceptible to external conditions. To address the challenges of severe background interference and feature dilution in crop small object detection within complex agricultural scenarios, this paper proposes an enhanced detection network, ACF-YOLO, based on YOLO11. First, an Aggregated Multi-scale Local-Global Attention (AMLGA) module is designed to enhance the feature representation of weak targets by fusing local details with global semantics. Second, a Context-Guided Fusion Module (CGFM) and a Soft-Neighbor Interpolation (SNI) strategy are introduced. Their synergy alleviates feature aliasing effects and ensures the precise alignment of deep semantic information with shallow spatial details. Furthermore, the Inner-MPDIoU loss function is employed to optimize the bounding box regression accuracy for non-rigid targets by incorporating geometric constraints and auxiliary scale factors. To verify the detection capability of the proposed method, we constructed a UAV Wheat Head Dataset (UWHD) and conducted extensive experiments on the UWHD, GWHD2021, and RFRB datasets. The experimental results demonstrate that ACF-YOLO outperforms other comparative methods, confirming its stable detection performance and contributing to the sustainable development of agriculture.

Accurate pest classification is critical for precision agriculture, yet existing deep learning methods face challenges including computational inefficiency from uniform sample processing and inadequate modeling of complex feature relationships. This paper proposes AdaptPest-Net, a task-adaptive architecture with three key innovations: (1) Sample-Difficulty-Aware Dynamic Routing (SDADR) employs Gaussian-gated path selection to adaptively route samples through shallow, medium, or deep networks based on predicted classification difficulty, improving accuracy by matching network capacity to sample complexity; (2) Graph Convolution-Mamba Fusion (GCMF) synergistically combines a 3-layer GCN with adaptive adjacency for explicit spatial structural modeling and a selective Mamba state-space model (T = 8, input-dependent parameters) for temporal feature dynamics, capturing complementary topological relationships and long-range dependencies through parallel dual-branch extraction; (3) Bidirectional Cross-Modal Attention enables deep integration between spatial and temporal modalities through mutual enhancement with batch-level knowledge transfer and adaptive gate fusion, where graph topology directly guides Mamba’s feature evolution. Comprehensive experiments on IP102 and D0 demonstrate that AdaptPest-Net achieves 78.4% accuracy on IP102, representing a significant improvement over existing methods, while D0 experiments validate strong cross-dataset generalization capability with 99.85% accuracy.