Next-generation high-throughput phenotyping with trait prediction through adaptable multi-task computational intelligence

Abstract

• AMULET represents a groundbreaking advancement in plant phenotyping. • AMULET integrates plant detection, prediction, segmentation, and data analysis. • AMULET identify the latent space of the phenom by using machine learning models. • AMULET demonstrates unparalleled adaptability across species. Phenotypes, which define an organism’s behaviour and physical attributes, result from the complex interplay of genetics, development, and environment. Predicting future plant traits is mainly challenging due to these dynamic interactions. This work presents AMULET, a modular approach that combines imaging-based high-throughput phenotyping and machine learning to predict morphological and physiological plant traits hours to days before they are visible. Trained with over 30,000 Arabidopsis thaliana plants, AMULET streamlines the phenotyping process by integrating plant detection, prediction, segmentation, and data analysis, enhancing workflow efficiency and reducing time. AMULET achieved impressive performance metrics, including a dice loss of 0.0104 and an IoU score of 0.9948 for the test set, indicating high accuracy in the segmentation task or an R 2 score of 0.9289 for descriptor estimation. Moreover, Simpler yet Better Video Prediction (SimVP) appeared as the most effective model in predicting plant growth and health status. Using phenotyping images from studies focused on the Arabidopsis thaliana-Pseudomonas syringae pathosystem, AMULET analysed the latent phenom by identifying traits restrictive to human perception and essential to understanding plant response to concrete growth conditions. Techniques like TorchGrad and Gradient-weighted Class Activation Mapping helped to reveal these new hidden traits. AMULET also demonstrated its adaptability by accurately detecting and predicting phenotypes of in vitro potato plants after minimal fine-tuning with just 100 plants. This versatile approach streamlines phenotyping and holds significant promise for improving breeding programs and agricultural management by enabling pre-emptive interventions optimising plant health and productivity.

Plant phenotyping relevance

AMULETは画像ベースのハイスループット植物表現型解析と機械学習による形態・生理形質の推定、予測、セグメンテーションを中核として開発・評価しており、方法論的貢献が明確です。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.