Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

Abstract

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

Plant phenotyping relevance

植物フェノタイピング用UAV画像の超解像モデルと前処理ワークフローを開発・比較検証しており、植物形質測定への適用性が中心的である。

Code and data availability

The paper describes a novel paired LR/HR aerial image dataset (canola, lentil, wheat) and an image pre-processing/registration pipeline used for super resolution-based phenotyping. The authors state an intent to release the dataset ('Our dataset will be made publicly available...'), but no public repository, deposit,或可

No evidence-backed public reproduction asset is currently recorded.