LightPM-DETR: a lightweight transformer for grading detection of rubber tree powdery mildew

Abstract Rubber tree ( Hevea brasiliensis ) powdery mildew (PM) is a devastating foliar disease causing annual yield losses exceeding 30% in severely affected plantations. Automated multi-grade detection in complex field conditions is hampered by three technical gaps: (i) CNN-based detectors lack global context for disambiguating fine-grained early lesions; (ii) Transformer detectors incur quadratic complexity $$O(n^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> impractical for edge deployment; and (iii) conventional multi-scale fusion inadequately bridges the semantic gap across disease scales. We propose LightPM-DETR , a lightweight detection transformer addressing these gaps through a co-designed architecture–compression pipeline. The encoder incorporates four modules: (1) Selective Feature Scanning (SFS) , a newly designed tri-stream Mamba adaptation for O ( n )-complexity long-range modelling; (2) Poly-Kernel Inception Net (PKI Net) , adapted for multi-scale receptive field aggregation; (3) Wavelet Efficient Attention (WEA) , a new Haar-basis frequency-domain key-value compression mechanism for lesion-sensitive attention; and (4) Global Query Aggregator (GQA) , a new hierarchical cross-attention module for query enrichment. Structured channel pruning followed by IoU-weighted knowledge distillation further compresses the model by 85.3% in parameters relative to RT-DETR-L (from 32.7 M to 4.8 M). On the self-built PM-Dataset-Plus (6-grade, 8,412 images, 47,836 instances) and PD40 (40-class cross-species) benchmarks, LightPM-DETR achieves mAP $$_{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mn>50</mml:mn> <mml:mrow/> </mml:mmultiscripts> </mml:math> of 91.6% and 84.3% respectively (mean over three runs), surpassing all baselines. The compressed variant operates at 4.8 M parameters and 6.2 G FLOPs, achieving 62.4 FPS on a desktop GPU and 23.8 FPS on an NVIDIA Jetson Orin Nano. Code and data will be released at https://github.com/wfcyliyuheng-dev/PM-Dataset-PLus upon acceptance.