Novel detection method of fertilizer discharge flow rate for centrifugal variable spreader based on improved lightweight YOLOv5s-seg

Variable fertilizer application technology is widely used in precision agriculture due to the sustained rapid development of global intelligent agriculture. Centrifugal variable fertilizer-spreading has become increasingly common in large-scale modern agricultural production because of its simple structure, wide spreading capability, high efficiency, and low cost. To improve the accuracy and uniformity of centrifugal fertilizer application, enhance the detection performance of the fertilizer discharge flow sensor, and facilitate effective real-time feedback and online adjustment of application rates, this study introduces an improved lightweight YOLOv5sseg network for a centrifugal fertilizer discharge flow detection system. The proposed method replaces the original YOLOv5s backbone network with a lightweight HGNetV2 network, effectively boosting detection speed and efficiency. Additionally, the model optimizes feature extraction for small granular fertilizers by refining the detection head, significantly reducing the number of model parameters and expanding the sensing field. By introducing an adaptable AKConv structure, the model becomes more versatile, accommodating fertilizers of varying sizes. The inclusion of the iRMB further enhances the model's sensitivity to granular fertilizer shapes, improving segmentation performance. Experimental results showed that the improved YOLOv5s-seg network achieved an FPS of 45 frames/s, a mAP of 95.9 %, a computation of 17.8 G, and a model size of 6.66 MB. Compared to other network models, the proposed model reduced size and computation volume while improving detection accuracy and speed, achieving optimal segmentation effects for granular fertilizers, making it easy to deploy on mobile devices. Validation experiments determined that TIT was set to 35 ms, and the fertilizer discharge port opening ranged from 17 to 25 mm, the total mass of particles counted per second exhibited a linear correlation, indicating stable fertilizer discharge. The maximum detection error between the total particle mass measured by the system and the actual fertilizer discharged was 5.07 %, with a flow rate measurement range of 15.13-54.83 g/s and a correlation coefficient of 0.9984. These results provide a new method for precise detection of centrifugal fertilizer discharge flow, offering a theoretical reference for real-time feedback control in variable fertilizer application technology within precision agriculture.