Accurate extraction of phenotypic traits from image data is essential for cereal crop research, but spike detection in greenhouses is challenging due to the environmental and physical similarities between spikes and leaves. Recent efforts include increasing image resolution and feature dimensionality, and developing neural networks such as SpikeSegNet to improve spike detection. However, these methods struggle to accurately localize small spikes,and further advances in neural network tuning and novel detection models are needed to efficiently overcome these spike detection challenges.