Detection of Eggplant Fruits and Stems in Complex Greenhouse Environments Using an Improved YOLOv8n

Abstract

Accurate perception of eggplant fruits and stems remains challenging for greenhouse harvesting robots because illumination changes, foliage occlusion, fruit overlap, and background branches can degrade target visibility, particularly for small and curved stems. To improve joint fruit-and-stem detection under these conditions, this study develops an enhanced YOLOv8n model using a greenhouse dataset collected across different illumination levels, viewpoints, occlusion degrees, and fruit-overlap situations. The baseline network was modified in three aspects. Selected conventional convolutions in the backbone and neck were replaced by Omni-Dimensional Dynamic Convolution (ODConv) to improve feature adaptation to targets with different scales and shapes. Efficient Multi-Scale Attention (EMA) was placed after the SPPF module to emphasize informative responses from fruit and stem regions while reducing background interference. In addition, C2f_MSBlock was incorporated into the neck to strengthen multi-scale feature representation and fusion. The resulting model achieved 96.4% precision, 97.2% recall, 99.0% mAP@0.5, and 86.0% mAP@0.5:0.95, with 3.74 M parameters, 6.5 GFLOPs, and a model size of 7.9 MB. Relative to the original YOLOv8n, these four detection metrics increased by 2.2, 0.3, 0.5, and 2.9 percentage points, respectively, while GFLOPs decreased by 16.7%. These results indicate that the modified model improves detection robustness in complex greenhouse scenes while maintaining moderate computational requirements, providing a feasible visual perception approach for eggplant fruit recognition and stem localization in robotic harvesting.