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A real-time ripeness detection model for tomatoes in complex greenhouse environments
Bo Cheng1,2, Xuli Wu3, Hao Du2
1College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China.
Frontiers in Plant Science
|June 15, 2026
Summary
This study introduces DDC-YOLOv11n for real-time tomato ripeness detection in greenhouses. The model significantly improves accuracy, aiding timely harvesting and crop management.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Machine Learning
Background:
- Timely tomato harvesting is crucial for quality and yield.
- Detecting tomato ripeness in complex greenhouse environments presents challenges due to varying lighting and occlusions.
Purpose of the Study:
- To develop a robust model for real-time tomato ripeness detection in challenging greenhouse settings.
- To enhance the accuracy and efficiency of automated harvesting systems.
Main Methods:
- Proposed DDC-YOLOv11n model integrating Zero-DCE for image enhancement.
- Incorporated an improved Deep Residual Shrinkage Network (DRSN) for noise reduction.
- Developed an enhanced CBAM (LKCBAM) module using dilated convolution and channel grouping for improved feature extraction.
Main Results:
- DDC-YOLOv11n demonstrated superior performance compared to the original YOLOv11n.
- Achieved significant improvements in mAP@0.5 (+16.8%), precision (+24.6%), recall (+8.3%), and F1 score (+18.1%).
- The model effectively handles image noise, occlusions, and dense scenes.
Conclusions:
- DDC-YOLOv11n is highly effective for real-time tomato ripeness detection in complex greenhouses.
- The proposed enhancements significantly boost detection accuracy and reliability.
- Findings support the integration of this model into automated harvesting and agricultural management systems.
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