A deep learning method for oriented and small wheat spike detection (OSWSDet) in UAV images.

• A deep learning method for oriented and small wheat spike detection was proposed to accurately detect wheat spikes in UAV images. • The method introduces the orientation feature of wheat spikes into the network and reduces the negative effect of background on detection based on circular smooth label. • The method can better solve tiny wheat spikes' occlusion and overlap problems. Detecting and characterizing spikes from wheat field images is essential in wheat growth monitoring for precision farming. Along with various technological developments, deep-learning-based methods have remarkably improved wheat spike detection performance. However, detecting small and overlapping wheat spikes in UAV images is still challenging because high spike occlusion and complex background can cause error detection and miss detection problems. This paper proposes a deep learning method for oriented and small wheat spike detection (OSWSDet). Unlike classical wheat spike detection methods, OSWSDet introduces the orientation of wheat spikes into the YOLO framework by integrating a circle smooth label (CSL) and a micro-scale detection layer. These improvements enhance the ability to detect small-sized wheat spikes and prevent wheat spike detection errors. The experiment results show that OSWSDet outperforms classical wheat spike detection methods, and the average accuracy (AP) is 90.5%. OSWSDet can accurately detect spikes in UAV images with complex field backgrounds and provides technical references for future field wheat phenotype monitoring. [ABSTRACT FROM AUTHOR]