# SCFM-DETR: an enhanced transformer-based method for automated maize disease detection in field environments

## TL;DR

A new lightweight model called SCFM-DETR improves maize disease detection in field conditions with high accuracy and reduced computational needs.

## Contribution

The novel SCFM-DETR model introduces an improved backbone and adaptive feature fusion module for efficient and accurate maize disease detection.

## Key findings

- SCFM-DETR achieves 96.7% average precision and 95.8% recall for maize disease detection.
- The model reduces parameters and computational load by 47% and 49%, respectively, compared to the baseline.
- The model is suitable for deployment in computationally limited agricultural environments.

## Abstract

Maize is susceptible to various diseases throughout its growth cycle, which can significantly reduce yields. The accurate identification of maize diseases with similar symptomatic manifestations is particularly challenging under field conditions due to heterogeneous lighting and variable weather conditions. This paper proposes a novel detection model named SCFM-DETR, which is based on an improved Real-Time DEtection TRansformer (RT-DETR) to achieve robust identification of maize diseases in complex environments. SimAM-StarNet is employed as the backbone for feature extraction in this model, reducing the number of parameters and improving multiscale feature fusion, thereby diminishing the impact of background noise. Furthermore, the original RepC3 module is replaced with a newly designed CGLU-FasterBlock-MANet (CFM) module, which enhances adaptive feature fusion for finer discriminative capability. The experimental results demonstrate that the SCFM-DETR model achieves an average precision of 96.7% and a recall of 95.8% on a maize disease dataset, exceeding the corresponding metrics of the baseline RT-DETR-R18 model by 3.1% and 6.0%. Additionally, the model reduces the number of parameters and computational load by 47% and 49%, respectively, making it highly suitable for deployment in computationally limited agricultural settings. This work offers a high-accuracy, lightweight framework that facilitates intelligent crop disease monitoring and supports the advancement of smart agriculture.

The online version contains supplementary material available at 10.1186/s13007-026-01507-8.