ENAS-BASED YOLOv8 IMPROVEMENT WITH JOINT STRUCTURE AND LOSS SEARCH FOR CORN SEEDLING AND WEED DETECTION

Abstract

Corn seedling and weed detection during the 2–5-leaf stage is essential for precision weeding, but similar morphology, field background interference, and the accuracy-efficiency trade-off limit the practical use of lightweight detectors. To address these problems, this study proposes Corn-Weed-ENAS, an improved YOLOv8-based detection model with joint structure and loss-function search. A differentiated ENAS search space was constructed for the backbone, neck, detection head, and loss function to automatically identify a compact architecture suitable for corn seedling-weed detection. A field image dataset of 2–5-leaf corn seedlings and weeds were collected in Heilongjiang Province, and comparative and ablation experiments were conducted against mainstream detectors. The proposed model achieved 97.3% mAP@0.5 with 2.5M parameters and 6.7 GFLOPs, showing a favorable balance between detection accuracy and lightweight deployment. To further verify its agricultural engineering applicability, an indoor simulated operation test was conducted on a Jetson Orin Nano Super platform using 20 pots of corn seedlings and weeds and a moving test vehicle at 1.2 m/s. In the virtual spraying signal verification, YOLOv8n produced five missed detections and one false detection, whereas Corn-Weed-ENAS produced three missed detections and no false detection. These results indicate that Corn-Weed-ENAS can provide more reliable weed localization and virtual trigger outputs for selective spraying decision support.