ArticleFrontiers in plant science2021
Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- New-generation rice seed germination assessment: high efficiency and flexibility via SeedRuler web-based platform.Frontiers in plant science · 2025Article
- RSG-YOLOV8: Detection of rice seed germination rate based on enhanced YOLOv8 and multi-scale attention feature fusion.PloS one · 2024Article
- Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield.Sensors (Basel, Switzerland) · 2022Article
- Evidence and Impacts of Nanoplastic Accumulation on Crop Grains.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
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9 authors.
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Abstract
The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.
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