Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Sungyul Chang; Unseok Lee; Min Jeong Hong; Yeong Deuk Jo; Jin-Baek Kim

doi:10.3389/fpls.2021.721512

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Front Plant Sci. 2021 Nov 11:12:721512. doi: 10.3389/fpls.2021.721512. eCollection 2021.

Authors

Sungyul Chang¹, Unseok Lee², Min Jeong Hong¹, Yeong Deuk Jo¹, Jin-Baek Kim¹

Affiliations

¹ Radiation Breeding Research Team, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup-si, South Korea.
² Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, South Korea.

Abstract

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15-21 DAS) and late (∼21-23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17-21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

Keywords: Arabidopsis thaliana; deep learning DL); growth modeling; high-throughput phenotyping (HTP); phenomics; plant biomass; time series analysis.