Improved YOLOv8-based tobacco plant counting across different terrain conditions

Accurate plant counting is essential for tobacco yield estimation and planting density regulation. However, manual quadrat surveys are inefficient and error-prone, and conventional detectors often suffer from feature loss and background confusion under complex field conditions. This study proposes an improved YOLOv8n framework, YOLOv8-AKConv-MLCA, tailored for UAV imagery of plain-field and mountain-field tobacco. First, an Alterable Kernel Convolution (AKConv) module is embedded inside the original C2f blocks to replace all conventional convolutions, enabling adaptive sampling and richer multi-scale representation of small and densely distributed targets. Second, a mixed local channel attention (MLCA) module is inserted between the last C2f-AKConv block and SPPF to fuse local spatial cues with global channel dependencies, suppressing clutter and occlusion effects. Extensive experiments on UAV datasets show that the proposed model achieves counting accuracies of 97.20% (plain-field) and 96.13% (mountain-field), improving over baseline YOLOv8 by 3.98% and 3.25%, respectively. Detection metrics likewise improve: mean average precision (mAP) reaches 0.936 and 0.914 in the two scenarios, surpassing SSD (0.844, 0.827), Faster R-convolutional neural network (0.865, 0.842), and a Transformer-based variant (YOLOv8-Trans, 0.923, 0.907). Relative to YOLOv8, maximum gains of 12.1% in precision, 1.9% in recall, and 7.3% in mAP are observed. Crucially, real-time throughput is preserved, with inference speeds of 219-227 frames per second across datasets. Grad-CAM visualizations further confirm that YOLOv8-AKConv-MLCA concentrates attention on canopy regions and suppresses background interference, offering intuitive evidence of enhanced feature learning. Overall, the proposed framework delivers a strong accuracy-efficiency trade-off and robust generalization under complex terrain, providing an effective solution for automated tobacco plant counting and supporting precision cultivation and smart agricultural management. Code and trained weights are available upon reasonable request for replication and evaluation.