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YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting.
Shuai Xiang1,2, Siyu Wang1,2, Mei Xu1,2
1College of Agronomy, Sichuan Agricultural University, 211-Huimin Road, Wenjiang District, Chengdu, 611130, People's Republic of China.
Plant Methods
|January 28, 2023
Summary
Accurate soybean pod counting is now possible with YOLO POD, a new AI model. This method enhances yield estimation and breeding by precisely detecting dense, overlapping pods faster than previous approaches.
Area of Science:
- Agricultural Science
- Computer Vision
- Machine Learning
Background:
- Soybean pod number is a key yield indicator, crucial for agriculture.
- Manual pod counting is time-consuming and labor-intensive.
- Existing object detection methods struggle with dense, overlapping soybean pods.
Purpose of the Study:
- To develop an accurate and efficient method for soybean pod counting.
- To improve upon existing object detection frameworks for dense object counting.
Main Methods:
- Proposed YOLO POD, an enhanced YOLO X framework.
- Incorporated a pod number prediction block and modified the loss function.
- Integrated the Convolutional Block Attention Module (CBAM) for improved feature extraction.
Main Results:
- YOLO POD achieved an R² of 0.967, outperforming YOLO X by 0.049.
- Minimal increase in inference time (0.08s) with enhanced accuracy.
- Low error metrics: MAE (4.18), MAPE (10.0%), RMSE (6.48), indicating high precision.
Conclusions:
- Achieved the first accurate automated soybean pod counting.
- Presented a novel solution for detecting and counting dense, overlapping objects in agriculture.
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