A Comparative Analysis of Object Detection Models for Plant Leaf Counting Under Overlapping Foliage Conditions

Abstract

Leaf counting has numerous applications in plant phenotyping, such as plant growth analysis, yield prediction, and disease detection. However, manual counting is labor-intensive and time-consuming, posing a significant limitation. To address this issue, deep learning-based object detection models are implemented for automated leaf counting. Existing works that explore automated leaf counting are limited due to the inherent structure of plants, e.g., plants are small and occur in overlapping clusters, causing the models to perform sub-optimally. Furthermore, the numerous amount of object detection models introduces a problem of choosing the proper model. Currently, there is limited understanding in determining which object detection architectures perform well for automated leaf counting, particularly when dealing with complex plant anatomy. To address the gap in the literature, three foundational object detection models with different characteristics are compared: YOLOv8, YOLOv12, and Faster R-CNN. The three models chosen display key distinctions such as one-stage vs. two-stage detections and convolution-based vs. attention-based. To provide comprehensive results, multiple optimization techniques were applied to each model. Our experimental results showed that YOLOv8 had the best performance on all performance metrics (mAP50, mAP5095, IOU, MAE, and MAPE). Specifically, YOLOv8 achieved an IOU of 0.6412, while YOLOv12 and Faster R-CNN only achieved an IOU of 0.567 and 0.606, respectively.

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