LRA-YOLOv11n: An Improved YOLOv11n Model for Field Detection of Mango Malformation Disease

Early detection and timely management of mango malformation disease (MMD) are essential for stabilizing yield and improving orchard management efficiency. However, existing mango disease-recognition studies have mainly focused on leaf diseases, fruit defects, or image-level classification, whereas object detection of MMD-related mango inflorescences under natural orchard conditions remains insufficiently investigated. This task is challenging because malformed inflorescences show large morphological variation, occur at different scales, and are frequently affected by branch occlusion, illumination variation, and complex orchard backgrounds. To address these challenges, this study proposes an improved YOLOv11n model, termed LRA-YOLOv11n, for field detection of MMD. YOLOv11n was used as the baseline. First, LSKNet was introduced to reconstruct the backbone network and provide a dynamic large-receptive-field mechanism for representing large-span and irregular malformed inflorescence contours. Second, receptive-field attention was incorporated into the neck network by constructing C3k2_RFAConv and RFAConv modules, thereby refining multiscale semantic features and suppressing background noise caused by overlapping branches, leaves, and shadows. Finally, an auxiliary detection branch was added during training to strengthen the learning of small or visually weak inflorescence features and blurred target boundaries through deep supervision. Tests on a self-built field dataset showed that the improved model achieved [email protected] and [email protected]:0.95 values of 82.4% and 49.4%, respectively, representing improvements of 5.0 and 3.4 percentage points over the baseline YOLOv11n. The model contained 5.57 M parameters and achieved competitive overall performance among models of comparable scale. These results indicate that LRA-YOLOv11n alleviates feature confusion, missed detection, and false detection in the tested orchard scenes while maintaining a moderate computational cost, providing preliminary algorithmic support for further evaluation of MMD detection in field images.