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Image analysis-based recognition and quantification of grain number per panicle in rice - PubMed
Source: pubmed.ncbi.nlm.nih.gov
Article summary:
1. The use of artificial intelligence (AI) in scientific research has become increasingly popular due to its ability to analyze large amounts of data quickly and accurately.
2. In the field of agriculture, AI can be used for image analysis-based recognition and quantification of grain number per panicle in rice, which can help improve crop yield and quality.
3. This approach involves using machine learning algorithms to analyze images of rice panicles and accurately count the number of grains present, providing valuable information for plant breeders and farmers.
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Topics for further research:
Image analysis for grain number per panicle in rice
Rice yield improvement through image analysis
Quantification of rice panicle traits using computer vision
Machine learning for rice crop management
High-throughput phenotyping of rice using image analysis
Automated rice grain counting using image processing techniques