Abstract During pineapple cultivation, detecting and counting the number of pineapple flowers in real time and estimating the yield are essential. Deep learning methods are more efficient in real-time performance than traditional manual detection. However, existing deep learning models are characterized by low detection speeds and cannot be applied in real time on mobile devices. This paper presents a lightweight model in which filter pruning compresses the YOLOv5 network. An adaptive batch normalization layer eva…