Automated pomegranate maturity detection facilitates yield enhancement and cost reduction; however, existing methods face significant challenges in complex natural environments, including difficulty in distinguishing green pomegranates from green foliage backgrounds and the trade-off between detection accuracy and computational efficiency. To address these issues, this paper proposes GLMF-DEIM, a lightweight pomegranate maturity detection algorithm. The proposed method is built upon the DEIM detection framework and incorporates a Gaussian-Haar Discrete Wavelet Transform module (GHDWStem) to achieve frequency-domain feature separation, effectively resolving the target-background similarity problem. The approach de-signs a Lightweight Adaptive Weight Downsampling module (LAWD) and Lightweight Frequency-Domain Dynamic Convolution Stages (LFDStages) to enable efficient feature extraction. Additionally, a Multi-level Feature Fusion Network (MFFN) is constructed to enhance multi-scale detection capabilities, while Dense O2O matching strategy and Matchability-Aware Loss are employed to optimize the training process. Extensive validation was conducted on a self-constructed datasets comprising 5,855 images that span five distinct growth stages of pomegranates. The proposed GLMF-DEIM model attains a 50% intersection-over-union (IoU) average precision (AP50) of 93.1%, an AP75 of 84.5%, and a small-object AP (APS) of 32.7%. These results correspond to relative improvements of 1.9%, 2.3%, and 1.3%, respectively, over the performance of the optimal baseline method. In terms of computational efficiency, GLMF-DEIM incurs only 16.9 GFLOPs of computational cost with 8.16 M parameters, achieving an effective trade-off between detection accuracy and inference efficiency that is well-suited for edge deployment scenarios in smart agriculture.
Wang et al. (Wed,) studied this question.