Phenomics for Komatsuna plant growth tracking using deep learning approach

Abstract

Plant growth tracking has potential applications in plant breeding research, crop management, understanding gene–environment interactions, stress analysis, and yield prediction. In this work, we determine plant phenotypes to track the plant’s growth over time. We use a publicly available Komatsuna plant dataset that includes time series color image sequences of Komatsuna plants captured over time to track plant growth. First, we use a deep neural network-based multi-class semantic segmentation algorithm to track each plant leaf in the image sequence. We achieved average multi-class semantic segmentation accuracy of 97.41%, MIoU of 0.9032 and dice coefficient of 0.9147 using DeepLabV3+ model. The model could track partially occluded and tiny emerging leaves throughout image sequences. In the second phase, we determine plant phenotypes like leaf count, projected plant area, emergence time of leaves using segmented images obtained in the first stage. We investigate the algorithm’s performance by evaluating and comparing plant phenotype values for segmented and ground-truth images. Growth curves indicate similar patterns for all the image sequences and demonstrate the method’s applicability for plant growth analysis.