Deep Learning for Stomatal Opening Recognition in Gynura formosana Kitam Leaves

Gynura formosana Kitam possesses beneficial properties such as heat-clearing, detoxification, and cough suppression, making it a highly nutritious plant with significant economic value. During its growth, the plant’s leaves are prone to infections that can impair stomatal function and hinder growth. Effective identification of stomatal openings and timely application of appropriate chemicals or hormones or indirect environmental adjustments (such as light, temperature, and humidity) to regulate stomatal openings are essential for maintaining the plant’s healthy growth. Currently, manual observation is the predominant method for monitoring stomatal openings of Gynura formosana Kitam, which is complex, labor-intensive, and unsuitable for automated detection. To address this, the study improves upon YOLOv8s by proposing a real-time, high-precision stomatal detection model, Refined GIoU. This model substitutes the original IoU evaluation methods in YOLOv8s with GIoU, DIoU, and EIoU while incorporating the SE (Squeeze-and-Excitation) and SA (Self-Attention) attention mechanisms to enhance understanding of feature representation and spatial relationships. Additionally, enhancements to the P2 layer improve the feature extraction and scale adaptation. The effectiveness of the Refined GIoU is demonstrated through training and validation on a dataset of 1500 images of Gynura formosana Kitam stomata. The results show that the Refined GIoU achieved an average precision (mAP) of 0.935, a recall of 0.98, and an F1-score of 0.88, reflecting an excellent overall performance. The GIoU loss function is better suited to detecting stomatal openings of Gynura formosana Kitam, significantly enhancing the detection accuracy. This model facilitates the automated, real-time monitoring of stomatal openings, allowing for timely control measures and improved economic benefits of Gynura formosana Kitam cultivation.