Improved YOLOv26-ACearly-ASPPdeep for citrus disease and pest detection

This study addresses early and accurate detection of citrus diseases and pests under complex orchard conditions, which is essential for timely intervention and yield protection. Existing detectors often struggle with small lesions, subtle early symptoms, and cluttered foliage backgrounds. We propose YOLOv26-ACearly-ASPPdeep, a lightweight one-stage detector that functionally decouples feature enhancement across network depth: AdaptiveConv (ACearly) is embedded in shallow C3k2 blocks to strengthen fine-grained, content-aware filtering before aggressive downsampling, while ASPP-based multi-branch context aggregation (ASPPdeep) is placed in deep backbone/neck stages to enlarge receptive-field diversity for irregular lesion morphology. SCDown modules further reduce information loss during downsampling. Experiments on a Roboflow citrus disease/pest dataset (4,358 images; blackspot, canker, greening, and healthy) show that the proposed model improves max precision from 0.75565 (baseline YOLOv26) to 0.78032 (+3.26%) and raises mean Average Precision at IoU = 0.5 (mAP@0.5) from 0.791 to 0.863. Under identical training settings, it also outperforms representative detectors including YOLOv5n, YOLOv7-tiny, YOLOv8n, and YOLOv11n in Precision, Recall, F1-score, and per-class AP, while remaining suitable for near real-time orchard monitoring (15.1 ms/image on an RTX 3090). The results indicate that depth-aware AdaptiveConv-ASPP coupling provides a practical accuracy-efficiency trade-off for smart citrus disease and pest surveillance.