The manual counting of marigold (Calendula officinalis L.) flowers is a labor-intensive task that compromises the accuracy of yield estimates and optimal harvest timing, especially in large-scale fields to know the harvesting moment. This study aimed to evaluate the performance of five versions of the YOLOv8 object detection model (Nano, Small, Medium, Large, and X-Large) for detecting and counting marigold flowers using high-resolution RGB images acquired by unmanned aerial vehicle (UAV). The experimental design included image acquisition with a multispectral drone, image clipping, annotation, and model training on Google Colab with the Adam optimizer. Performance metrics such as precision, recall, mAP50, mAP50–95, and classification loss were analyzed, alongside model correlation with manual counting through Pearson’s, RMSE, MAE, and R². The Large model demonstrated the best performance, achieving over 90% precision and mAP50 and an R² of 0.895. Although the X-Large model offered similar accuracy, it required significantly more computational resources. In contrast, the small model emerged as a computationally efficient alternative with performance comparable to the larger models. The findings demonstrate the feasibility of integrating UAV-based imagery and YOLOv8 for accurate, automated flower detection, reducing subjectivity and labor in floriculture management. This approach shows promise for broader applications in precision agriculture, especially for crops with small, dense floral structures.
Barboza et al. (Wed,) studied this question.