Towards the synthesis of spectral imaging and machine learning-based approaches for non-invasive phenotyping of plants.

Abstract

High-throughput phenotyping is now central to the progress of plant sciences, accelerated breeding, and precision farming. The power of phenotyping comes from the automated, rapid, non-invasive collection of large datasets describing plant objects. In this context, the goal of extracting relevant information from different kinds of images is of paramount importance. We review both the spectral and machine learning-based approaches to imaging of plants for the purpose of their phenotyping. The advantages and drawbacks of both approaches will be discussed with a focus on the monitoring of plants. We argue that an emerging approach combining the strengths of the spectral and the machine learning-based approaches will remain a promising direction in plant phenotyping in the nearest future.

Plant phenotyping relevance

植物フェノタイピングのためのスペクトル画像解析と機械学習手法を中心にレビューしており、方法論的レビューに該当する。

Code and data availability

This is a review article with no paper-specific phenotyping datasets, images, code, or models. Its Data Availability statement is request-only ('The data are available from the corresponding author on reasonable request'), and all cited datasets (e.g., Fuji-SfM, MinneApple) are prior third-party works, not assets of or

No evidence-backed public reproduction asset is currently recorded.