An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning

Abstract

Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.