AdaptiveLeaf: Lightweight Multi-Scale Framework for Small-Target Detection of Maize Leaf Diseases

Early-stage maize leaf diseases and pests are difficult to detect due to their small size, low contrast, and complex backgrounds. AdaptiveLeaf is a lightweight multi-scale framework designed to improve the detection of such small targets. It integrates an Adaptive Kernel Lightweight Block (AKL-Block) for dynamic multi-scale feature extraction, a Feature Decomposition and Reconstruction (FDR) module to recover fine details such as lesion edges and spore clusters, and a Scale-Aware Gradient Boosting Loss (SAGB-Loss) to increase the training contribution of small targets. Experiments on 10,324 field-collected maize leaf images across eight disease and pest categories show that AdaptiveLeaf achieves a mean mAP@0.5 of 75.0% over three repeated runs and increases small-target AP from 28.1% to 32.8%, using only 2.52 M parameters and 5.3 GFLOPs. The framework balances accuracy and efficiency, making it suitable for real-time field inspection and precision agriculture.