To address the limitations of current agricultural image recognition tasks, such as isolated models, weak multi-task synergy, insufficient real-time performance and generalization capability, this study develops an intelligent agricultural image recognition system based on deep learning and multi-task fusion. This system adopts a "device-edge-cloud" three-level collaborative architecture. By constructing a multi-task joint learning framework, the system enables concurrent training and collaborative inference for both crop height measurement and disease recognition. The system fuses the feature extraction capabilities of mobile convolutional neural networks (MobileNet) and visual transformers (ViT) with a coordinate attention (CA) mechanism; this design enhances key feature extraction, suppresses complex farmland background interference while significantly alleviating computational burden. Experimental results show that the system achieves remarkable performance across both core tasks. For crop height measurement, the determination coefficient R2 between the algorithm′s measurement results and the manual measured values reaches up to 99.98%. For disease recognition, the optimized lightweight model achieves an average detection accuracy of 98.76%, with only 12.64 M parameters. The average single-image inference latency is as low as 9.1 ms, demonstrating excellent potential for embedded deployment. Moreover, the system exhibits high stability in complex environments, maintaining measurement deviation of crop height within ±2 cm. This precision allows for seamless integration into low-altitude monitoring platforms, such as unmanned aerial vehicles (UAVs), to satisfy the real-time data processing requirements of low-altitude agricultural operations. In summary, the proposed system delivers excellent scalability, real-time efficiency, and robustness, effectively overcoming traditional bottlenecks in multi-task synergy, lightweight deployment, and adaptation to complex environments. It provides an integrated solution for multi-dimensional image recognition in smart agriculture empowered by the low-altitude economy, thereby facilitating the large-scale implementation of precision agriculture while improving agricultural production efficiency.

agricultural image recognition  /  multi-task learning  /  lightweight networks  /  attention mechanisms  /  YOLO algorithm  /  Agricultural monitoring at low altitudes