An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation.

Abstract

High-throughput phenotyping of yield-related traits is meaningful and necessary for rice breeding and genetic study. The conventional method for rice yield-related trait evaluation faces the problems of rice threshing difficulties, measurement process complexity, and low efficiency. To solve these problems, a novel intelligent system, which includes an integrated threshing unit, grain conveyor-imaging units, threshed panicle conveyor-imaging unit, and specialized image analysis software has been proposed to achieve rice yield trait evaluation with high throughput and high accuracy. To improve the threshed panicle detection accuracy, the Region of Interest Align, Convolution Batch normalization activation with Leaky Relu module, Squeeze-and-Excitation unit, and optimal anchor size have been adopted to optimize the Faster-RCNN architecture, termed 'TPanicle-RCNN,' and the new model achieved F1 score 0.929 with an increase of 0.044, which was robust to indica and japonica varieties. Additionally, AI cloud computing was adopted, which dramatically reduced the system cost and improved flexibility. To evaluate the system accuracy and efficiency, 504 panicle samples were tested, and the total spikelet measurement error decreased from 11.44 to 2.99% with threshed panicle compensation. The average measuring efficiency was approximately 40 s per sample, which was approximately twenty times more efficient than manual measurement. In this study, an automatic and intelligent system for rice yield-related trait evaluation was developed, which would provide an efficient and reliable tool for rice breeding and genetic research.

Plant phenotyping relevance

イネの収量関連形質を高スループットに取得する統合計測・画像解析システムを開発し、検出精度と測定効率を検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The article explicitly states that all threshed panicle training and testing data (1,072 threshed panicle images with PASCAL VOC annotations, augmented to 3,432 training and 856 testing images) are publicly available via a Baidu Netdisk link with an extraction code for non-commercial research. This is a paper-specific,