Leaf Navigated Plant Growth Measurement for Automated Plant Cultivation System

This manuscript describes a workflow for automatically measuring plant growth from scanned images. To explore the different conditions of plant growth, botanists sow seeds in trays and use imaging devices such as scanners or cameras to record the daily growth of plants. Previous methods behave poorly in root measuring. To design an automated plant growth measurement system, we propose a leaf-root distance-based plant organ measurement method that finds the paths of the root by minimizing the distance energy from the root endpoints to the leaves. This method contains three modules. The first is a deep learning-based plant detection module to detect individual plants' regions of interest (ROI). An organ mask generation module that uses Otsu-thresholding to segment root and leaf. A plant organ analysis module to generate plant phenotypes (e.g., length of primary and lateral roots) from mask images. The experimental results showed that the proposed method had a higher measurement accuracy than previous methods.