LysipheN: a gravimetric IoT device for near real-time high-frequency crop phenotyping: a case study on common beans

Abstract

Climate instability directly affects agro-environments. Water scarcity, high air temperature, and changes in soil biota are some factors caused by environmental changes. Verified and precise phenotypic traits are required for assessing the impact of various stress factors on crop performance while keeping phenotyping costs at a reasonable level. Experiments which use a lysimeter method to measure transpiration efficiency are often expensive and require complex infrastructures. This study presents the development and testing process of an automated, reliable, small, and low-cost prototype system using IoT with high-frequency potential in near-real time. Because of its waterproofness, our device—LysipheN—assesses each plant individually and can be deployed for experiments in different environmental conditions (farm, field, greenhouse, etc.). LysipheN integrates multiple sensors, automatic irrigation according to desired drought scenarios, and a remote, wireless connection to monitor each plant and device performance via a data platform. During testing, LysipheN proved to be sensitive enough to detect and measure plant transpiration, from early to ultimate plant developmental stages. Even though the results were generated on common beans, the LysipheN can be scaled up/adapted to other crops. This tool serves to screen transpiration, transpiration efficiency, and transpiration-related physiological traits. Because of its price, endurance, and waterproof design, LysipheN will be useful in screening populations in a realistic ecological and breeding context. It operates by phenotyping the most suitable parental lines, characterizing genebank accessions, and allowing breeders to make a target-specific selection using functional traits (related to the place where LysipheN units are located) in line with a realistic agronomic background.