Abstract
This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.
Highlights
While there has been much work and progress in developing automated, commercial phenotyping systems in controlled environments (e.g., LemnaTech Conveyor Scanalyzer, Phenospex TraitFinder), there remains a need to continue developing systems for phenotyping at larger field scales
The resources factor ratings were highly variable, which is to be expected as each intuition and project has their own available equipment, field locations, and facilities, and there were no significant correlations between the resources factor and any other factor included in the complexity analysis
Several observations can be made from the complexity and utility score assessments of the FB-HTP systems, which include: (i) lack of information and development related to data handling, transfer, and storage; (ii) underdeveloped HMI for phenotyping system operators; and (iii) competing contributions from factors towards the total system complexity and utility scores
Summary
While there has been much work and progress in developing automated, commercial phenotyping systems in controlled environments (e.g., LemnaTech Conveyor Scanalyzer, Phenospex TraitFinder), there remains a need to continue developing systems for phenotyping at larger field scales. Field-based systems that employ proximal sensing approaches enable data collection at high spatial resolutions necessary for measuring a variety of morphological and physiological traits in realistic growing conditions over an entire growing season. These FB-HTP systems commonly utilize image, spectral, and climate sensors to collect data at the plant, row, or plot level in crop systems, operating with varying levels of autonomy. The capabilities of technology used for high-throughput phenotyping have improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems To address this issue, this work presents and implements a framework for characterizing system utility and complexity and evaluates FB-HTP systems through a meta-analysis to identify bottlenecks in adoption, and identifying areas for future work in system improvement
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