Zhenyuan Sun ¹ Zhi Yang ^2* Yimin Ding ^1* Boyan Sun ^1* Saiju Sun ^1* Zhen Guo ^1* Lei Zhu ^1*

• ¹ Ningxia University, Yinchuan, China
• ² Institute of Water Resources Research of Ningxia Hui Autonomous Region, yinchuan, China

Counting crop plants is a laborious and error-prone undertaking in agricultural science, particularly crucial for yield forecasting, field management, and experimental studies. Varying altitude of UAV can affect the dimensions of early maize seedlings in captured images, potentially resulting in blurred features and reduced counting accuracy. To address this issue, we developed a deep learning methodology based on DINO, called RC-Dino, which is specifically designed for the purpose of counting seedlings in UAV-acquired images. The efficacy of our approach was validated using a field-collected dataset of early maize seedlings. Firstly, we introduced a novel self-calibrating convolutional layer, RSCconv, which can be readily incorporated into existing architectural frameworks. The RSCconv is designed to calibrate spatial domain features, thereby improving the representation of early maize seedlings compared to non-seedlings elements within the feature maps. To further enhance the discriminability of early maize seedlings, we have devised a novel module for adaptive spatial feature fusion (ASCFF). This module adaptively fuses feature maps from different layers extracted from the backbone network. Furthermore, we employed transfer learning to integrate the pre-trained weights of the backbone with RSCconv, facilitating accelerated convergence and enhanced accuracy. A dataset, designated the Early Maize Seedlings Dataset (EMSD), was assembled comprising 1,233 images of early maize seedlings, with annotations for a total of 83,404 seedlings. The results of our testing on this dataset demonstrated that our proposed method exhibited the highest performance for the counting of early maize seedlings. In particular, the AP and Recall were increased by 16.29% and 8.19%, respectively, in comparison to the DINO model. In terms of seedling counting, our approach demonstrated a coefficient of determination (R²) in different datasets that was superior to the performance of other models, including Faster R-CNN, RetinaNet, YOLOX, and Deformable DETR. Therefore, our approach is suitable for the accurate counting of early maize seedlings in the field. By integrating RSCconv and ASCFF into the Faster R-CNN, RetinaNet and Deformable DETR models, we have improved their detection and counting accuracy, further validating the effectiveness of our method. The RSCconv and ASCFF code mentioned in this paper can be found here: https://github.com/collapser-AI/RC-Dino.