A Unified Detection Network with Multi-Scale Attention Fusion for Sugarcane Leaf Disease Identification
TL;DR
UniDetNet-MAF is presented, a lightweight unified detection framework that simultaneously localizes and classifies sugarcane leaf diseases and is suitable for timely, field-scale sugarcane disease diagnosis.
Abstract
Sugarcane yield is reduced by leaf diseases such as mosaic, red rot, rust, and yellow leaf, whose overlapping symptoms make early field diagnosis difficult. This paper presents UniDetNet-MAF, a lightweight unified detection framework that simultaneously localizes and classifies sugarcane leaf diseases. Three components drive its performance: MAF-Conv (a multi-dimensional attention convolution acting jointly on input-channel, kernel-spatial, kernel-count, and output-channel axes), Hierarchical Fusion Block (HFB) and MSAF-Net (a HFB coupled with a multi-scale attention fusion network providing bidirectional feature aggregation), and SAC-Loss (a shape-aware constrained bounding-box regression loss that improves localisation of irregular lesions). Experiments on the publicly available Sugarcane Leaf Disease Dataset, consisting of 2,521 images across five classes, namely Healthy, Mosaic, Red Rot, Rust, and Yellow Leaf, show that UniDetNet-MAF achieves 94.23% accuracy, 95.12% precision, 93.87% recall, 94.51% F1-score, and 93.68% Matthews Correlation Coefficient (MCC), along with mAP@0.5 = 0.937 and 112 FPS on a single GPU, an improvement of roughly 11% over the LeafNet baseline across all classification metrics. The framework is therefore suitable for timely, field-scale sugarcane disease diagnosis.