Flexible and high quality plant growth prediction with limited data.

Abstract

Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent years, however, there are still two issues. First, image-based plant growth prediction is currently taken either from time series or image generation viewpoints, resulting in a flexible learning framework and clear predictions, respectively. Second, deep learning-based algorithms are notorious to require a large-scale dataset to obtain a competing performance but collecting enough data is time-consuming and expensive. To address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms. To be more specific, we raise a new framework with a length-changeable time-series processing unit to generate images flexibly. A generative adversarial loss is utilized to optimize our model to obtain high-quality images. Furthermore, we first recognize three key points to perform time-series data augmentation and then put forward T-Mixup and T-Copy-Paste. T-Mixup fuses images from a different time pixel-wise while T-Copy-Paste makes new time-series images with a different background by reusing individual leaves extracted from the existing dataset. We perform our method in a public dataset and achieve superior results, such as the generated RGB images and instance masks securing an average PSNR of 27.53 and 27.62, respectively, compared to the previously best 26.55 and 26.92.

Plant phenotyping relevance

植物画像から成長状態を予測・生成する深層学習フレームワークと、時系列データ拡張手法を開発しており、画像およびインスタンスマスクによる表現型取得・解析が研究の中心である。

Code and data availability

The paper's plant growth prediction experiments use the public KOMATSUNA plant phenotyping dataset (Uchiyama et al., 2017), which the authors explicitly state is publicly available via the linked IEEE document. No author analysis code, trained models, or paper-specific supplementary data assets are described with a de-