Accurate and efficient tomato ripeness detection is essential for automated harvesting and yield estimation in greenhouse environments. However, this task remains challenging due to three main factors: complex illumination conditions in greenhouse scenarios lead to noticeable color variation, clustered growth results in fruit occlusion, and high-accuracy models often fail to meet real-time requirements. To address these issues, the study proposes a high-performance detection model, CICE-YOLO, introduces four innovations. First, we introduce a Color Shift Estimation-and-Correction (CSEC) module to generate pseudo-normal exposure feature maps and separately estimate color deviations in bright and dark regions, thereby mitigating color distortion caused by illumination variations. Second, the YOLOv10n backbone is changed to integrate the Cross-stage Heterogeneous (CHet) module with heterogeneous convolutions to enhance fruit feature extraction. Third, an Improved Bidirectional Feature Pyramid Network (IBiFPN) with learnable fusion weights is adopted to achieve adaptive multi-scale feature aggregation, improving the detection of occluded and partially visible fruits. Finally, the CICE-YOLO detection head relies on the Efficient Intersection over Union (EIoU) loss to decouple width and height errors for more accurate bounding-box regression. Experiments conducted on a self-built dataset containing supplemental nighttime lighting, occlusion, forward lighting, and backlighting scenarios demonstrate that CICE-YOLO achieves an mAP50 of 85.9%, showing a 4.2 percentage points improvement over the baseline. The model size is 6.2 MB, and the inference time per image after offline illumination correction is 15.1 ms, which meets the real-time detection requirements. Furthermore, evaluations on a yellow-tomato dataset confirm the model robustness ability of the proposed method.
Lu et al. (Sun,) studied this question.