Benchmarking an automated minirhizotron camera system

Abstract

Understanding plant roots and root development is key for agricultural productivity as roots affect many plant, ecosystem and biogeochemical processes. However, studying root development in-situ can be challenging. Minirhizotrons are commonly used for root monitoring, and allow non-destructive tracking of root development overtime. Nonetheless, existing commercial minirhizotron cameras are expensive and manually operated. Planar optodes are a promising technology for quantifying concentrations of soil solutes, but have not yet been paired with minirhizotron technology. We developed an inexpensive, automated minirhizotron camera system, the RhizoPi camera, built using off-the-shelf computer components that can be paired with planar optodes. The purpose of this study was to evaluate the capability and utility of the RhizoPi camera system. The objectives were to 1) assess the capabilities and design of the RhizoPi minirhizotron camera system and benchmark it against a commercial minirhizotron camera; 2) create an image analysis script to analyze minirhizotron images and compare results to those of existing commercial software; and 3) develop a method for using the RhizoPi camera system for planar optode imaging. Objectives 1 and 2 are the focus of Chapter 2 in which soybean (Glycine max L.) was grown in containers under controlled greenhouse conditions. Images collected using the RhizoPi camera system were analyzed for percentage of images that are roots (root percentage) using a script written in the Python programming language and the RootSnap!® software. Although the average root percentage measured by the Python script, 3.36%, was significantly larger than with RootSnap!, 2.97%, the difference was small in magnitude (0.39%). Images collected using the RhizoPi camera were compared to images collected on the same day using the commercially-available CID Bioscience CI-600 minirhizotron camera. Images from the two camera systems were processed to ensure the exact image frame location was being compared, then processed using RootSnap!. Objective 3 was the focus of Chapter 3, in which a method is presented for augmenting the RhizoPi minirhizotron camera system with planar oxygen optode technology and techniques. In this method acrylic minirhizotron tubes were turned into oxygen sensitive planar optodes by applying oxygen-sensitive dyes in a strip along the length of each tube. Successful calibrations of these optodes are presented as a proof of concept for this method. The RhizoPi camera system is capable of collecting research-quality images of roots, and can serve as a platform for deploying planar optode technologies for in situ analysis of soil solution chemistry.