Research on Asparagus Recognition Based on Deep Learning

In recent years, there have been several outbreaks of extensive flooding in northern provinces, and asparagus stem blight disease brown spot diseases become increasingly serious, significantly reducing the yield grown large fields, so production has developed toward facility agriculture. order to solve problems high labor cost, shortage, low efficiency faced by agriculture, it is an inevitable trend adopt mechanized harvesting for asparagus. The prerequisite target detection. Compared with algorithms such as Faster R-CNN, which need use features candidate regions classification recognition cannot meet real-time requirements harvesting, this paper proposes a YOLO-based scheme that can quickly perform detection accuracy 85.45% enhanced interference resistance, greatly improve agriculture accelerate mechanization process