An Online Method for Detecting Seeding Performance Based on Improved YOLOv5s Model

Abstract

Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion.