Research on cabbage transplanting status detection and operation quality evaluation in complex environments based on improved YOLOv10-TQ and DeepSort
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an AI-powered system for monitoring cabbage transplanting quality. The method accurately detects and counts seedlings in open fields, improving agricultural efficiency and yield prediction.
Area Of Science
- Agricultural Engineering
- Computer Vision
- Machine Learning
Background
- Crop transplanting quality significantly impacts plant survival and yield.
- Mechanized transplanting faces challenges with manual inspection efficiency and traditional algorithm accuracy.
- Open-field environments present difficulties for current seedling detection and tracking methods.
Purpose Of The Study
- To develop a robust detection-and-tracking method for assessing cabbage transplanting quality in real-world conditions.
- To enhance the accuracy and stability of identifying various transplanting states (normal, soil-buried, bare-root).
- To achieve precise and reliable counting of transplanted seedlings for improved agricultural management.
Main Methods
- An improved YOLOv10-TQ network with triplet attention and QFocal Loss-cross-entropy was utilized for enhanced seedling state detection.
- A lightweight MobileViT feature extractor was integrated into the DeepSort algorithm for improved target tracking.
- A line-crossing counting strategy was implemented for accurate seedling enumeration and identity de-duplication.
Main Results
- The proposed method achieved a mean average precision (mAP) of 86.3% for seedling state detection.
- An average counting accuracy of 97.8% was recorded on a custom cabbage transplanting dataset.
- The system demonstrated superior detection accuracy, tracking stability, and counting precision over traditional methods.
Conclusions
- The developed AI-driven approach offers a significant advancement in the intelligent quality evaluation of cabbage transplanting.
- The method provides a strong technical foundation for data-driven decision-making in agricultural machinery systems.
- A visualization system was created to monitor transplanting status, promoting precision agriculture.
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