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Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images
Xiaodong Bai1, Pichao Liu2, Zhiguo Cao3
1School of Computer Science and Technology, Hainan University, Haikou 570228, China.
Plant Phenomics (Washington, D.C.)
|April 11, 2023
Summary
This study introduces RiceNet, an automated method using unmanned aerial vehicles (UAVs) for accurate rice plant counting. RiceNet significantly outperforms traditional manual counting and other methods, improving efficiency in rice production applications.
Area of Science:
- Agricultural Science
- Computer Vision
- Remote Sensing
Background:
- Manual rice plant counting is labor-intensive and time-consuming.
- Accurate plant counts are vital for yield estimation, growth monitoring, and disaster assessment in rice production.
Purpose of the Study:
- To develop an automated, efficient, and accurate method for rice plant counting, locating, and sizing.
- To reduce the workload associated with traditional manual counting methods.
Main Methods:
- Utilized an unmanned aerial vehicle (UAV) to capture RGB images of rice fields.
- Proposed RiceNet, a deep learning model with a feature extractor and three decoders (density map, location, size).
- Incorporated a rice plant attention mechanism and positive-negative loss for improved accuracy.
Main Results:
- Developed a new UAV-based rice counting dataset with 355 images and 257,793 labeled points.
- RiceNet achieved mean absolute error (MAE) of 8.6 and root mean square error (RMSE) of 11.2.
- Demonstrated superior performance compared to state-of-the-art methods on three different crop datasets.
Conclusions:
- RiceNet offers an accurate and efficient alternative to manual rice plant counting.
- The proposed method has the potential to revolutionize rice production management.
- Automated plant counting using UAVs and deep learning is a viable approach for agricultural applications.
