The spike is a key component of wheat, and its orientation is an important phenotypic trait. However, the traditional manual measurement of spike orientation is time-consuming and labor-intensive. To address this issue, we developed a spike orientation detection model based on deep learning. The model is trained on a large dataset of wheat spikes, and it can accurately detect the orientation of wheat spikes.
The study area is located at the Baima Experimental Station (119°18′71″E, 31°62′00″N) of Nanjing Agricultural University in China. A total of 960 plots were cultivated during the period spanning 2022 to 2025. The experimental design involved two replications, adhering to the principles of the split-plot design. Each plot occupied an area of 1.5 m × 1.5 m and consisted of six rows of wheat, with a row spacing of 0.25 m. The distance between each plot was 0.5 m.
