Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery

The aim of this study is to propose a lightweight YOLOv8n maize seedling detection algorithm that incorporates multi-scale features to address the problems of large number of model parameters and computation, low real-time performance, and small detection range of the existing maize seedling detection models during plant detection. By fusing RepConv with HGNetV2 using the idea of reparameterisation, a RepHGBlock structure is designed to form a new lightweight backbone network, RepHGNetV2, ; BiFPN is introduced into the neck network portion of the model to enhance the interactive fusion of bidirectional information flow between multiple scales and hierarchies; and a fusion task decomposition, dynamic convolutional alignment is designed, DFL (Distribution Focal Loss) ideas, TDADH, a task dynamically aligned detection head, which uses shared convolution and dynamically aligns the tasks of classification and localization to extract features; and Grad-CAM++ technique is used to generate a heat map for model detection, visualize effective features of the target and understand the model focus region. The experimental results show that the improved model achieves a detection accuracy of 96. 5%, which is basically the same as the original model. The weight size, number of parameters, and computational FLOPs are reduced to 3. 5 MB, 1. 58 M, and 7. 4 G, respectively, which are reduced by about 43%, 47%, and 8. 6%. The frame rate FPS is only reduced from 149. 98 to 146. 3, a reduction of about 2. 4%. The results show that the lightweight model has high recognition accuracy, speed and low complexity, which is more suitable for practical deployment in resource-constrained edge devices, UAVs, and embedded systems, and is able to provide technical support for the precise management of maize during the seedling stage of drip irrigation water-fertilizer integration.