A novel method for intelligent analysis of rice yield traits based on LED transmission imaging and cloud computing

Abstract

Rice yield traits extraction is necessary for rice breeding and genetic analysis, in which the discrimination of filled and unfilled grain was the bottleneck. The traditional methods including wind separating and water sinking are inefficient, unreliable, and poorly reproducible, meanwhile the machine vision methods based on X-ray and 3D structured imaging have the disadvantages of high cost, ionizing radiation, and poor scalability. To address the above problems, this study has proposed a novel method for intelligent analysis of rice yield traits based on LED transmission imaging and cloud application. The study has found that the red-light LED transmission imaging was able to obtain high-contrast images of filled and unfilled grains and had the advantages of radiation-free, low-cost, and high-efficiency imaging. Using 3,200 rice grain images as the training set, the results showed that the YOLOv5 model outperformed the Faster RCNN, RetinaNet, SSD, and Cascade RCNN models, and its filled and unfilled grain recognition precision was 98.94% and 90.96% on the 800 test sets, while the recall rate was 97.91% and 94.94%. To further improve the recognition accuracy of the YOLOv5 model, five model optimization methods were adopted in the study, including TPH, CBAM, adding a small target detection layer, using Bi-FPN and Multi-NMS. The results proved that the accuracy of unfilled grain recognition had significantly improved, the recognition precision increased by 4.54% to 95.44%, and the recall rate increased by 0.77% to 95.71%. Finally, this study has designed the specified algorithms based on the recognized rectangular box, which could accurately measure filled and unfilled grain numbers, seed setting rate and grain shape parameters. Moreover, the study has designed a local Qt software and a cloud-based website, which can support the simultaneous input of multiple images with high real-time performance and good scalability, providing a practical novel tool for rice breeding and genetic analysis.