Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping

Abstract

Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the high level of diversity in these researchers’ backgrounds, it is likely that they have differing user needs from a fine-grained plant feature annotation system. We conducted semi-structured interviews with eight experienced annotators from diverse backgrounds, and observed how they interact with their preferred annotation system, to elucidate the challenges faced when annotating plant features and identify user needs. We collected qualitative responses to the interview questions, and conducted a quantitative evaluation of the agreement of their annotations on the given images. By analyzing the participants’ behaviors and the collected data, we identified common user needs and derived implications for the design of an AI-assisted annotation system, including providing a range of annotation options, the flexibility to adapt annotations, and functions to help addressing uncertainty. Our research contributes to the design of systems that make annotations efficient and reliable, not only benefiting plant phenotyping, but also other interdisciplinary fields that rely on user-driven annotations.

Code and data availability

This interview/annotation study used participants' own datasets and images from the PRMI dataset (a cited prior work, not a paper-specific asset). No author-deposited phenotype data, images, code, or models with public availability statements appear in the supplied blocks.

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