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


$$O(n^2)$$


O
(

n
2

)




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


$$_{50}$$



50





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.