Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging.

Abstract

Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.

Plant phenotyping relevance

UAV画像とUNetによってイネの倒伏状態を抽出・評価する手法を開発し、RGBおよびマルチスペクトル画像で性能検証しているため、植物表現型取得が中心である。

Code and data availability

The paper's UNet training/analysis code for rice lodging segmentation is explicitly stated to be publicly available at the authors' GitHub repository. No public dataset or image deposit is mentioned; the UAV imagery and annotations are not stated as shared.